Package 'xpose4'

Title:	Diagnostics for Nonlinear Mixed-Effect Models
Description:	A model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.
Authors:	Andrew C. Hooker [aut, cre, cph], Mats O. Karlsson [aut, cph], Justin J. Wilkins [aut], E. Niclas Jonsson [aut, trl, cph], Ron Keizer [ctb] (functionality for bootstrap of GAM and SCM)
Maintainer:	Andrew C. Hooker <[email protected]>
License:	LGPL (>= 3)
Version:	4.7.3
Built:	2025-01-27 05:41:47 UTC
Source:	https://github.com/uupharmacometrics/xpose4

Help Index

The Xpose Package
Model comparison plots, of absolute differences in goodness-of-fit predictors against covariates, for Xpose 4
Absolute conditional weighted residuals vs covariates for Xpose 4
Absolute population conditional weighted residuals vs population predictions for Xpose 4
Absolute value of the conditional weighted residuals vs. population predictions, conditioned on covariates, for Xpose 4
Absolute population weighted residuals vs population predictions, and absolute individual weighted residuals vs individual predictions, for Xpose 4
box and whisker plots of the absolute value of the individual weighted residuals vs. covariates
absolute value of the individual weighted residuals vs. the independent variable
Absolute individual weighted residuals vs individual predictions for Xpose 4
Absolute individual weighted residuals vs individual predictions, conditioned on covariates, for Xpose 4
Absolute individual weighted residuals vs population predictions or independent variable for Xpose 4
Absolute weighted residuals vs covariates for Xpose 4
Absolute value of (C)WRES vs. independent variable plot in Xpose4.
Absolute population weighted residuals vs population predictions for Xpose 4
Absolute population weighted residuals vs population predictions, conditioned on covariates, for Xpose 4
Column-transformation functions for Xpose 4
Print tables or text in a grid object
Additional model comparison plots, for Xpose 4
Additional goodness-of-fit plots, for Xpose 4
Autocorrelation of conditional weighted residuals for Xpose 4
autocorrelation of the individual weighted residuals
Autocorrelation of weighted residuals for Xpose 4
Basic goodness-of-fit plots, for Xpose 4
Basic model comparison plots, for Xpose 4
Function to create histograms of results from the bootstrap tool in PsN
Print summary information for a bootgam or bootscm
Import bootscm data into R/Xpose
Categorical observations vs. independent variable using stacked bars.
Categorical (visual) predictive check.
Functions changing variable definitions in Xpose 4
Functions changing miscellaneous parameter settings in Xpose 4
Change parameter scope.
Changes the name of an Xpose data item
Changes the label of an Xpose data item
Change Xpose variable definitions.
Compute the Conditional Weighted Residuals
Plot scatterplot matrices of parameters, random parameters or covariates
Create xpose.multiple.plot class.
This function creates the Xpose data classes ("xpose.data" and "xpose.prefs")
Histogram of conditional weighted residuals (CWRES), for Xpose 4
Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose 4
Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose 4
Population conditional weighted residuals (CWRES) plotted against the independent variable (IDV) for Xpose 4
Box-and-whisker plot of conditional weighted residuals vs the independent variable for Xpose 4
Population conditional weighted residuals (CWRES) plotted against population predictions (PRED) for Xpose 4
Box-and-whisker plot of conditional weighted residuals vs population predictions for Xpose 4
Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the independent variable (IDV)
Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the population predictions (PRED)
Extract or assign data from an xpose.data object.
Check through the source dataset to detect problems
Prints the contents of an Xpose data object
Change in individual objective function value vs. covariate value.
Change in Objective function value vs. removal of individuals.
Change in individual objective function value 1 vs. individual objective function value 2.
Observations (DV), individual predictions (IPRED) and population predictions (IPRED) plotted against the independent variable (IDV), for Xpose 4
Observations (DV) plotted against the independent variable (IDV) for Xpose 4
Observations (DV) plotted against individual predictions (IPRED) for Xpose 4
Dependent variable vs individual predictions, conditioned on covariates, for Xpose 4
Dependent variable vs individual predictions, conditioned on independent variable, for Xpose 4
Observations (DV) plotted against population predictions (PRED) for Xpose 4
Dependent variable vs population predictions, conditioned on covariates, for Xpose 4
Dependent variable vs population predictions, conditioned on independent variable, for Xpose 4
Observations (DV) are plotted against individual predictions (IPRED) and population predictions (PRED), for Xpose 4
Exports Xpose graphics settings to a file.
Exports Xpose variable definitions to a file from an Xpose data object.
GAM functions for Xpose 4
Structured goodness of fit diagnostics.
Imports Xpose graphics settings from a file to an Xpose data object.
Imports Xpose variable definitions from a file to an Xpose data object.
Observations (DV), individual predictions (IPRED) and population predictions (PRED) are plotted against the independent variable for every individual in the dataset, for Xpose 4
Histograms of weighted residuals for each individual in an Xpose data object, for Xpose 4
Quantile-quantile plots of weighted residuals for each individual in an Xpose data object, for Xpose 4
Individual predictions (IPRED) plotted against the independent variable (IDV) for Xpose 4
Histogram of individual weighted residuals (IWRES), for Xpose 4
Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4
Individual weighted residuals (IWRES) plotted against the independent variable (IDV) for Xpose 4
Kaplan-Meier plots of (repeated) time-to-event data
Make stacked bar data set.
Function to plot the coverage of the Numerical Predictive Check
Extract or set the value of the Nsim slot.
Plot the parameter or covariate distributions using a histogram
Plot the parameter or covariate distributions using quantile-quantile (Q-Q) plots
Summarize individual parameter values and covariates
Parameters plotted against covariates, for Xpose 4
Plot parameters vs other parameters
Population predictions (PRED) plotted against the independent variable (IDV) for Xpose 4
Print an Xpose multiple plot object.
Function to create a histogram of results from the randomization test tool (randtest) in PsN
Random parameters plotted against covariates, for Xpose 4
Read NONMEM output files into Xpose 4
Read NONMEM table files produced from simulation.
Reading NONMEM table files
Read the results file from a Numerical or Visual Predictive Check run in PsN
Read (repeated) time-to-event simulation data files.
Read the vpctab file from PsN into Xpose
Resets Xpose variable definitions to factory settings
Print run summary in Xpose 4
Simulated prazosin Xpose database.
Function to create files for the simulated prazosin example in Xpose
Tabulate the population parameter estimates
Histogram of weighted residuals (WRES), for Xpose 4
Quantile-quantile plot of weighted residuals (WRES), for Xpose 4
Weighted residuals (WRES) plotted against covariates, for Xpose 4
Population weighted residuals (WRES) plotted against the independent variable (IDV) for Xpose 4
Box-and-whisker plot of weighted residuals vs the independent variable for Xpose 4
Population weighted residuals (WRES) plotted against population predictions (PRED) for Xpose 4
Box-and-whisker plot of weighted residuals vs population predictions for Xpose 4
Extract and set labels for Xpose data items.
Compare parameter estimates for covariate coefficients
Correlations between covariate coefficients
Distribution of difference in AIC
Plot of model size distribution for a bootgam or bootscm
Distribution of difference in OFV
OFV difference (optimism) plot.
Default function for calculating dispersion in xpose.gam.
Trace plots for conditional indices
Trace plots for conditional indices rper replicate number
Inclusion frequency plot
Inclusion frequency plot for combination of covariates.
Inclusion stability plot A plot of the inclusion frequency of covariates vs bootgam/bootscm iteration number. This plot can be used to evaluate whether sufficient iterations have been performed.
Plot of inclusion index of covariates.
Inclusion index individuals, compare between covariates.
Individual inclusion index
Define a scope for the gam. Used as default input to the scope argument in xpose.gam
Title
Create an Xpose data object
Class xpose.data
Stepwise GAM search for covariates on a parameter (Xpose 4)
Displays the Xpose license and citation information
Functions to create nice looking axes when using Log scales.
Create and object with class "xpose.multiple.plot".
Class for creating multiple plots in xpose
Xpose 4 generic function for plotting multiple lattice objects on one page
Default box-and-whisker panel function for Xpose 4
Default panel function for Xpose 4
Default histogram panel function for Xpose 4
Default QQ panel function for Xpose 4
Scatterplot matrix panel function for Xpose 4
The generic Xpose functions for box-and-whisker plots
The Xpose 4 generic functions for continuous y-variables.
The Xpose 4 generic functions for continuous y-variables.
The generic Xpose functions for QQ plots
The Xpose 4 generic functions for scatterplot matrices.
Class "xpose.prefs"
Summarize an xpose database
Print a pretty string.
Visual Predictive Check (VPC) using XPOSE
Xpose Visual Predictive Check (VPC) for both continuous and Limit of Quantification data.
Xpose visual predictive check for categorical data.
Classic menu system for Xpose 4
Extract or set the value of the Subset slot.
Extract and set Xpose variable definitions.

Xpose is an R-based model building aid for population analysis using NONMEM. It facilitates data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison.

Details

Xpose takes output from NONMEM output and/or PsN output and generates graphs or other analyses. It is assumed that each NONMEM run can be uniquely identified by a run number (see section below for how to generate the appropriate input to Xpose). Xpose is implemented using the lattice graphics library.

The Xpose package can be divided up into six subsections (functions associated with each of the different subsections are linked in the "See Also" section):

Data Functions: Functions for managing the input data and manipulating the Xpose database.
Generic Functions: Generic wrapper functions around the lattice functions. These functions can be invoked by the user but require quite detailed instructions to generate the desired output.
Specific Functions: These functions are single purpose functions that generate specific output given only the Xpose database as input. The behavior can, to some extent, be influenced by the user.
Classic Functions: Xpose has a text based menu interface to make it simple for the user to invoke the Xpose specific functions. This interface is called Xpose Classic. Given the limitations a text based interface imposes, Xpose Classic is not very flexible but may be useful for quick assessment of a model and for learning to use Xpose.
PsN Functions: These functions are the interface between Xpose and PsN, i.e. they do not post-process NONMEM output but rather PsN output.
GAM Functions: Functions take an Xpose object and performs a generalized additive model (GAM) stepwise search for influential covariates on a single model parameter.

How to make NONMEM generate input to Xpose

Xpose recognizes NONMEM runs, and files associated to a particular run, though the run number. This is a number that is used in the name of NONMEM model files, output files and table files. The fundamental input to Xpose is one or more NONMEM table files. These table files should be named as below followed by the run number, for example xptab1 for run number 1. Xpose looks for files according to the following pattern, where * is your run number:

sdtab* Standard table file, containing ID, IDV, DV, PRED, IPRED, WRES, IWRES, RES, IRES, etc.

patab* Parameter table, containing model parameters - THETAs, ETAs and EPSes

catab* Categorical covariates, e.g. SEX, RACE

cotab* Continuous covariates, e.g. WT, AGE

extra*, mutab*, mytab*, xptab*, cwtab* Other variables you might need to have available to Xpose

run*.mod Model specification file

run*.lst NONMEM output

Strictly, only one table file is needed for xpose (for example sdtab* or xptab*). However, using patab*, cotab*, catab* will influence the way that Xpose interprets the data and are recommended to get full benefit from Xpose.

You can use code in NONMEM similar to the following to generate the tables you need. NONMEM automatically appends DV, PRED, WRES and RES unless NOAPPEND is specified. Don't forget to leave at least one blank line at the end of the NONMEM model specification file.

$TABLE ID TIME IPRED IWRES EVID MDV NOPRINT ONEHEADER FILE=sdtab1 $TABLE ID CL V2 KA K SLP KENZ NOPRINT ONEHEADER FILE=patab1 $TABLE ID WT HT AGE BMI PKG NOPRINT ONEHEADER FILE=cotab1 $TABLE ID SEX SMOK ALC NOPRINT ONEHEADER FILE=catab1

Author(s)

E. Niclas Jonsson, Mats Karlsson, Justin Wilkins and Andrew Hooker

References

PsN

Examples


## Not run: 
# run the classic interface
library(xpose4)
xpose4()
  
# command line interface  
library(xpose4)
xpdb <- xpose.data(5)
basic.gof(xpdb)

## End(Not run)

## Not run: 
# run the classic interface
library(xpose4)
xpose4()
  
# command line interface  
library(xpose4)
xpdb <- xpose.data(5)
basic.gof(xpdb)

## End(Not run)

Model comparison plots, of absolute differences in goodness-of-fit predictors against covariates, for Xpose 4

Description

These functions plot absolute differences in PRED, IPRED, WRES, CWRES and IWRES against covariates for two specified model fits.

Usage

absval.dcwres.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "CWRES|")),
  main = "Default",
  ...
)

absval.dipred.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "IPRED|")),
  main = "Default",
  ...
)

absval.diwres.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "IWRES|")),
  main = "Default",
  ...
)

absval.dpred.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "PRED|")),
  main = "Default",
  ...
)

absval.dwres.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "WRES|")),
  main = "Default",
  ...
)
absval.dcwres.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "CWRES|")),
  main = "Default",
  ...
)

absval.dipred.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "IPRED|")),
  main = "Default",
  ...
)

absval.diwres.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "IWRES|")),
  main = "Default",
  ...
)

absval.dpred.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "PRED|")),
  main = "Default",
  ...
)

absval.dwres.vs.cov.model.comp(
  object,
  object.ref = NULL,
  type = NULL,
  ylb = expression(paste("|", Delta, "WRES|")),
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`object.ref`	An xpose.data object. If not supplied, the user will be prompted.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Conditional weighted residuals (CWRES) may require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of plots comprising comparisons of PRED, IPRED, WRES (or CWRES) and IWRES for the two specified runs.

Functions

absval.dcwres.vs.cov.model.comp(): The absolute differences in individual predictions against covariates for two specified model fits.
absval.dipred.vs.cov.model.comp(): The absolute differences in individual predictions against covariates for two specified model fits.
absval.diwres.vs.cov.model.comp(): The absolute differences in individual weighted residuals against covariates for two specified model fits.
absval.dpred.vs.cov.model.comp(): The absolute differences in population predictions against covariates for two specified model fits.
absval.dwres.vs.cov.model.comp(): The absolute differences in population weighted residuals against covariates for two specified model fits.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)

## A basic dWRES plot, without prompts
absval.dwres.vs.cov.model.comp(xpdb5, xpdb6)

## Custom colours and symbols, no user IDs
absval.dpred.vs.cov.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)

## End(Not run)


## Not run: 
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)

## A basic dWRES plot, without prompts
absval.dwres.vs.cov.model.comp(xpdb5, xpdb6)

## Custom colours and symbols, no user IDs
absval.dpred.vs.cov.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)

## End(Not run)

Absolute conditional weighted residuals vs covariates for Xpose 4

Description

This creates a stack of box and whisker plot of absolute population conditional weighted residuals (|CWRES|) vs covariates, and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the codexpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.cwres.vs.cov.bw(object, xlb = "|CWRES|", main = "Default", ...)
absval.cwres.vs.cov.bw(object, xlb = "|CWRES|", main = "Default", ...)

Arguments

`object`	An xpose.data object.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `xpose.plot.bw`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling box-and-whisker plots are available. See xpose.plot.bw for details.

Value

Returns a stack of box-and-whisker plots of |CWRES| vs covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

absval.cwres.vs.cov.bw(xpdb)


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

absval.cwres.vs.cov.bw(xpdb)

Absolute population conditional weighted residuals vs population predictions for Xpose 4

Description

This is a plot of absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.cwres.vs.pred(object, idsdir = "up", type = "p", smooth = TRUE, ...)
absval.cwres.vs.pred(object, idsdir = "up", type = "p", smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns an xyplot of |CWRES| vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.cwres.vs.pred(xpdb)

## A conditioning plot
absval.cwres.vs.pred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.cwres.vs.pred(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, no IDs
absval.cwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.cwres.vs.pred(xpdb)

## A conditioning plot
absval.cwres.vs.pred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.cwres.vs.pred(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, no IDs
absval.cwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)

Absolute value of the conditional weighted residuals vs. population predictions, conditioned on covariates, for Xpose 4

Description

This is a plot of absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED) conditioned by covariates, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.cwres.vs.pred.by.cov(
  object,
  covs = "Default",
  ylb = "|CWRES|",
  type = "p",
  smooth = TRUE,
  idsdir = "up",
  main = "Default",
  ...
)
absval.cwres.vs.pred.by.cov(
  object,
  covs = "Default",
  ylb = "|CWRES|",
  type = "p",
  smooth = TRUE,
  idsdir = "up",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`covs`	A vector of covariates to use in the plot. If "Default" the the covariates defined in `object@Prefs@Xvardef$Covariates` are used.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

The main argument is not supported owing to the multiple plots generated by the function.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of xyplots of |CWRES| vs PRED, conditioned on covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


absval.cwres.vs.pred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

absval.cwres.vs.pred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

Absolute population weighted residuals vs population predictions, and absolute individual weighted residuals vs individual predictions, for Xpose 4

Description

This is a matrix plot of absolute population weighted residuals (|CWRES|) vs population predictions (PRED) and absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the absval.cwres.vs.pred and absval.iwres.vs.ipred functions.

Usage

absval.iwres.cwres.vs.ipred.pred(object, main = "Default", ...)

absval.iwres.wres.vs.ipred.pred(object, main = "Default", ...)
absval.iwres.cwres.vs.ipred.pred(object, main = "Default", ...)

absval.iwres.wres.vs.ipred.pred(object, main = "Default", ...)

Arguments

`object`	An xpose.data object.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

The plots created by the absval.wres.vs.pred and absval.iwres.vs.ipred functions are presented side by side for comparison.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a compound plot.

Functions

absval.iwres.wres.vs.ipred.pred(): absolute population weighted residuals (|WRES|) vs population predictions (PRED) and absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED)

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.wres.vs.ipred.pred(xpdb)
absval.iwres.cwres.vs.ipred.pred(xpdb)

## Custom colours and symbols
absval.iwres.cwres.vs.ipred.pred(xpdb, cex=0.6, pch=8, col=1)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.wres.vs.ipred.pred(xpdb)
absval.iwres.cwres.vs.ipred.pred(xpdb)

## Custom colours and symbols
absval.iwres.cwres.vs.ipred.pred(xpdb, cex=0.6, pch=8, col=1)

box and whisker plots of the absolute value of the individual weighted residuals vs. covariates

Description

box and whisker plots of the absolute value of the individual weighted residuals vs. covariates

Usage

absval.iwres.vs.cov.bw(object, xlb = "|iWRES|", main = "Default", ...)
absval.iwres.vs.cov.bw(object, xlb = "|iWRES|", main = "Default", ...)

Arguments

`object`	An "xpose.data" object.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`main`	A string giving the plot title or `NULL` if none.
`...`	Other arguments passed to `xpose.panel.default`.

Value

An xpose.multiple.plot object

absolute value of the individual weighted residuals vs. the independent variable

Description

absolute value of the individual weighted residuals vs. the independent variable

Usage

absval.iwres.vs.idv(
  object,
  ylb = "|iWRES|",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)
absval.iwres.vs.idv(
  object,
  ylb = "|iWRES|",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)

Arguments

`object`	An "xpose.data" object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`idsdir`	a string indicating the directions of the extremes to include in labelling. Possible values are "up", "down" and "both".
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`...`	Other arguments passed to `xpose.panel.default`.

Value

A lattice object

Absolute individual weighted residuals vs individual predictions for Xpose 4

Description

This is a plot of absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.iwres.vs.ipred(
  object,
  ylb = "|iWRES|",
  type = "p",
  ids = FALSE,
  idsdir = "up",
  smooth = TRUE,
  ...
)
absval.iwres.vs.ipred(
  object,
  ylb = "|iWRES|",
  type = "p",
  ids = FALSE,
  idsdir = "up",
  smooth = TRUE,
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`ids`	Should id values be displayed?
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns an xyplot of |IWRES| vs IPRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.vs.ipred(xpdb)

## A conditioning plot
absval.iwres.vs.ipred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.iwres.vs.ipred(xpdb, main="My conditioning plot", ylb="|IWRES|", xlb="IPRED")

## Custom colours and symbols, no IDs
absval.iwres.vs.ipred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)


## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.vs.ipred(xpdb)

## A conditioning plot
absval.iwres.vs.ipred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.iwres.vs.ipred(xpdb, main="My conditioning plot", ylb="|IWRES|", xlb="IPRED")

## Custom colours and symbols, no IDs
absval.iwres.vs.ipred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)

Absolute individual weighted residuals vs individual predictions, conditioned on covariates, for Xpose 4

Description

This is a plot of absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED) conditioned by covariates, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.iwres.vs.ipred.by.cov(
  object,
  ylb = "|IWRES|",
  idsdir = "up",
  type = "p",
  smooth = TRUE,
  main = "Default",
  ...
)
absval.iwres.vs.ipred.by.cov(
  object,
  ylb = "|IWRES|",
  idsdir = "up",
  type = "p",
  smooth = TRUE,
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of xyplots of |IWRES| vs IPRED, conditioned by covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.vs.ipred.by.cov(xpdb)

## Custom axis labels
absval.iwres.vs.ipred.by.cov(xpdb, ylb="|IWRES|", xlb="IPRED")

## Custom colours and symbols, no IDs
absval.iwres.vs.ipred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)

## End(Not run)
## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.vs.ipred.by.cov(xpdb)

## Custom axis labels
absval.iwres.vs.ipred.by.cov(xpdb, ylb="|IWRES|", xlb="IPRED")

## Custom colours and symbols, no IDs
absval.iwres.vs.ipred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)

## End(Not run)

Absolute individual weighted residuals vs population predictions or independent variable for Xpose 4

Description

This is a plot of absolute individual weighted residuals (|IWRES|) vs individual predictions (PRED) or independent variable (IDV), specific functions in Xpose 4. These functions are wrappers encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.iwres.vs.pred(
  object,
  ylb = "|IWRES|",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)
absval.iwres.vs.pred(
  object,
  ylb = "|IWRES|",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns an xyplot of |IWRES| vs PRED or |IWRES| vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.vs.pred(xpdb)

## A conditioning plot
absval.iwres.vs.pred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.iwres.vs.pred(xpdb, main="My conditioning plot", ylb="|IWRES|", xlb="PRED")

## Custom colours and symbols, no IDs
absval.iwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)



## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.iwres.vs.pred(xpdb)

## A conditioning plot
absval.iwres.vs.pred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.iwres.vs.pred(xpdb, main="My conditioning plot", ylb="|IWRES|", xlb="PRED")

## Custom colours and symbols, no IDs
absval.iwres.vs.pred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)

Absolute weighted residuals vs covariates for Xpose 4

Description

This creates a stack of box and whisker plot of absolute population weighted residuals (|WRES| or |iWRES|) vs covariates. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from the xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.wres.vs.cov.bw(object, xlb = "|WRES|", main = "Default", ...)
absval.wres.vs.cov.bw(object, xlb = "|WRES|", main = "Default", ...)

Arguments

`object`	An xpose.data object.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `xpose.plot.bw`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

A wide array of extra options controlling box-and-whisker plots are available. See xpose.plot.bw for details.

Value

Returns a stack of box-and-whisker plots of |WRES| vs covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.cov.bw(xpdb)

## A custom plot
absval.wres.vs.cov.bw(xpdb, bwdotcol="white", 
  bwdotpch=15,
  bwreccol="red",
  bwrecfill="red",
  bwumbcol="red",
  bwoutpch=5,
  bwoutcol="black")

## A vanilla plot using IWRES
absval.iwres.vs.cov.bw(xpdb)

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.cov.bw(xpdb)

## A custom plot
absval.wres.vs.cov.bw(xpdb, bwdotcol="white", 
  bwdotpch=15,
  bwreccol="red",
  bwrecfill="red",
  bwumbcol="red",
  bwoutpch=5,
  bwoutcol="black")

## A vanilla plot using IWRES
absval.iwres.vs.cov.bw(xpdb)

## End(Not run)

Absolute value of (C)WRES vs. independent variable plot in Xpose4.

Description

This is a plot of the absolute value of the CWRES (default, other residuals as an option) vs independent variable, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from the xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.wres.vs.idv(
  object,
  idv = "idv",
  wres = "Default",
  ylb = "Default",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)
absval.wres.vs.idv(
  object,
  idv = "idv",
  wres = "Default",
  ylb = "Default",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)

Arguments

`object`	An xpose.data object.
`idv`	the independent variable.
`wres`	Which weighted residual to use. `"Default"` is the CWRES.
`ylb`	Y-axis label.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns an xyplot of |CWRES| vs idv (often TIME, defined by xvardef).

Author(s)

Andrew Hooker

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.idv(xpdb)

## A conditioning plot
absval.wres.vs.idv(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.wres.vs.idv(xpdb, main="Hello World", ylb="|CWRES|", xlb="IDV")

## Custom colours and symbols
absval.wres.vs.idv(xpdb, cex=0.6, pch=3, col=1)

## using the NPDEs instead of CWRES
absval.wres.vs.idv(xpdb,wres="NPDE")

## subsets
absval.wres.vs.idv(xpdb,subset="TIME<10")


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.idv(xpdb)

## A conditioning plot
absval.wres.vs.idv(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.wres.vs.idv(xpdb, main="Hello World", ylb="|CWRES|", xlb="IDV")

## Custom colours and symbols
absval.wres.vs.idv(xpdb, cex=0.6, pch=3, col=1)

## using the NPDEs instead of CWRES
absval.wres.vs.idv(xpdb,wres="NPDE")

## subsets
absval.wres.vs.idv(xpdb,subset="TIME<10")

Absolute population weighted residuals vs population predictions for Xpose 4

Description

This is a plot of absolute population weighted residuals (|WRES|) vs population predictions (PRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.wres.vs.pred(
  object,
  ylb = "|WRES|",
  idsdir = "up",
  type = "p",
  smooth = TRUE,
  ...
)
absval.wres.vs.pred(
  object,
  ylb = "|WRES|",
  idsdir = "up",
  type = "p",
  smooth = TRUE,
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns an xyplot of |WRES| vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.pred(xpdb)

## A conditioning plot
absval.wres.vs.pred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.wres.vs.pred(xpdb, main="My conditioning plot", 
  ylb="|WRES|", xlb="PRED")

## Custom colours and symbols
absval.wres.vs.pred(xpdb, cex=0.6, pch=19, col=1, 
  smcol="blue", smlty=2)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.pred(xpdb)

## A conditioning plot
absval.wres.vs.pred(xpdb, by="HCTZ")

## Custom heading and axis labels
absval.wres.vs.pred(xpdb, main="My conditioning plot", 
  ylb="|WRES|", xlb="PRED")

## Custom colours and symbols
absval.wres.vs.pred(xpdb, cex=0.6, pch=19, col=1, 
  smcol="blue", smlty=2)

Absolute population weighted residuals vs population predictions, conditioned on covariates, for Xpose 4

Description

This is a plot of absolute population weighted residuals (|WRES|) vs population predictions (PRED) conditioned by covariates, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.wres.vs.pred.by.cov(
  object,
  ylb = "|WRES|",
  type = "p",
  smooth = TRUE,
  ids = FALSE,
  idsdir = "up",
  main = "Default",
  ...
)
absval.wres.vs.pred.by.cov(
  object,
  ylb = "|WRES|",
  type = "p",
  smooth = TRUE,
  ids = FALSE,
  idsdir = "up",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`ids`	Logical. Should id labels on points be shown?
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of xyplots of |WRES| vs PRED, conditioned on covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.pred.by.cov(xpdb)

## Custom axis labels
absval.wres.vs.pred.by.cov(xpdb, ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
absval.wres.vs.pred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.pred.by.cov(xpdb)

## Custom axis labels
absval.wres.vs.pred.by.cov(xpdb, ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
absval.wres.vs.pred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

Column-transformation functions for Xpose 4

Description

These functions transform existing Xpose 4 data columns, adding new columns.

Usage

add.absval(object, listall = TRUE, classic = FALSE)

add.dichot(object, listall = TRUE, classic = FALSE)

add.exp(object, listall = TRUE, classic = FALSE)

add.log(object, listall = TRUE, classic = FALSE)

add.tad(object, classic = FALSE)
add.absval(object, listall = TRUE, classic = FALSE)

add.dichot(object, listall = TRUE, classic = FALSE)

add.exp(object, listall = TRUE, classic = FALSE)

add.log(object, listall = TRUE, classic = FALSE)

add.tad(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`listall`	A logical operator specifying whether the items in the database should be listed.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

These functions may be used to create new data columns within the Xpose data object by transforming existing ones.

Value

An xpose.data object (classic == FALSE) or null (classic == TRUE).

Functions

add.absval(): Create a column containing the absolute values of data in another column.
add.dichot(): Create a categorical data column based on a continuous data column
add.exp(): Create an exponentiated version of an existing variable
add.log(): Create a log transformation of an existing variable
add.tad(): Create a time-after-dose (TAD) data item based upon the dose and time variables in the dataset.

Author(s)

Niclas Jonsson, Justin Wilkins and Andrew Hooker

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Create a column containing the absolute values of data in another 
## column
add.absval(xpdb5)

## Create a categorical data column based on a continuous data column, 
## and do not list variables 
add.dichot(xpdb5, listall = FALSE)

## Create a column containing the exponentiated values of data in 
## another column
add.exp(xpdb5)

## Create a column containing log-transformations of data in another 
## column
add.log(xpdb5)

## Create a time-after-dose column
add.tad(xpdb5)

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Create a column containing the absolute values of data in another 
## column
add.absval(xpdb5)

## Create a categorical data column based on a continuous data column, 
## and do not list variables 
add.dichot(xpdb5, listall = FALSE)

## Create a column containing the exponentiated values of data in 
## another column
add.exp(xpdb5)

## Create a column containing log-transformations of data in another 
## column
add.log(xpdb5)

## Create a time-after-dose column
add.tad(xpdb5)

## End(Not run)

Print tables or text in a grid object

Description

These functions take an array of values and labels or an array of text and add it to one or many grid viewports in an orderly fashion.

Usage

add.grid.table(
  txt,
  col.nams = NULL,
  ystart,
  xstart = unit(0, "npc"),
  start.pt = 1,
  vp,
  vp.num = 1,
  minrow = 5,
  cell.padding = 0.5,
  mult.col.padding = 1,
  col.optimize = TRUE,
  equal.widths = FALSE,
  space.before.table = 1,
  center.table = FALSE,
  use.rect = FALSE,
  fill.type = NULL,
  fill.col = "grey",
  cell.lines.lty = 0,
  ...
)
add.grid.table(
  txt,
  col.nams = NULL,
  ystart,
  xstart = unit(0, "npc"),
  start.pt = 1,
  vp,
  vp.num = 1,
  minrow = 5,
  cell.padding = 0.5,
  mult.col.padding = 1,
  col.optimize = TRUE,
  equal.widths = FALSE,
  space.before.table = 1,
  center.table = FALSE,
  use.rect = FALSE,
  fill.type = NULL,
  fill.col = "grey",
  cell.lines.lty = 0,
  ...
)

Arguments

`txt`	The text or table values to add to the grid object.
`col.nams`	the column names of the table values
`ystart`	The y location to start printing in the grid viewport
`xstart`	The x location to start printing in the grid viewport
`start.pt`	The start point (row) in the table array to start printing
`vp`	The viewport(s) to add the table or text to
`vp.num`	the viewport number in `vp` to start printing to
`minrow`	The minimum rows before printing more columns to use in the table
`cell.padding`	padding between cells in the table
`mult.col.padding`	padding between multiple columns in the table
`col.optimize`	should we column optimize (`TRUE`) or row optimize (`FALSE`)
`equal.widths`	Should all columns have equal widths
`space.before.table`	Should there be a space before the table
`center.table`	should we center the table in the viewport?
`use.rect`	Should we make rectangles with background color around the table entries `TRUE` or `FALSE`
`fill.type`	Which rectangles should be filled. Allowed values are `"all"`, `"top"`, `"side"`, `"both"` and `NULL`.
`fill.col`	The color of the filled rectangles
`cell.lines.lty`	The line-type for the lines between the cells, using the same values as lty.
`...`	Other arguments passed to the various functions.

Value

A List is returned with the following components

`ystart`	new starting point for new text
`stop.pt`	null if everything gets printed
`vp.num`	the viewport needed for next text printed
`xpose.table`	A grob object that can be plotted.

Author(s)

Andrew Hooker

Additional model comparison plots, for Xpose 4

Description

This creates a stack of four plots, comparing absolute values of PRED, absolute values of IPRED, delta CWRES (or WRES) and delta IWRES estimates for the two specified model fits.

Usage

add.model.comp(
  object,
  object.ref = NULL,
  onlyfirst = FALSE,
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  main = "Default",
  force.wres = FALSE,
  ...
)
add.model.comp(
  object,
  object.ref = NULL,
  onlyfirst = FALSE,
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  main = "Default",
  force.wres = FALSE,
  ...
)

Arguments

`object`	An xpose.data object.
`object.ref`	An xpose.data object. If not supplied, the user will be prompted.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot. The default is TRUE.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`force.wres`	Should we use the WRES in the plots instead of CWRES (logical `TRUE` or `FALSE`)
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Four model comparison plots are displayed in sequence.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of plots comprising comparisons of absolute values of PRED, absolute values of IPRED, absolute differences in CWRES (or WRES) and absolute differences in IWRES for the two specified runs.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)

## A vanilla plot, without prompts
add.model.comp(xpdb5, xpdb6, prompt = FALSE)

## Custom colours and symbols, no user IDs
add.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)

## End(Not run)


## Not run: 
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)

## A vanilla plot, without prompts
add.model.comp(xpdb5, xpdb6, prompt = FALSE)

## Custom colours and symbols, no user IDs
add.model.comp(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)

## End(Not run)

Additional goodness-of-fit plots, for Xpose 4

Description

This is a compound plot consisting of plots of weighted population residuals (WRES) vs population predictions (PRED), absolute individual weighted residuals (|IWRES|) vs independent variable (IDV), WRES vs IDV, and weighted population residuals vs log(IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the wres.vs.pred, iwres.vs.idv and wres.vs.idv functions.

Usage

addit.gof(
  object,
  type = "p",
  title.size = 0.02,
  title.just = c("center", "top"),
  main = "Default",
  force.wres = FALSE,
  ...
)
addit.gof(
  object,
  type = "p",
  title.size = 0.02,
  title.just = c("center", "top"),
  main = "Default",
  force.wres = FALSE,
  ...
)

Arguments

`object`	An xpose.data object.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`title.size`	Amount, in a range of 0-1, of how much space the title should take up in the plot)
`title.just`	how the title should be justified
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`force.wres`	Plot the WRES even if other residuals are available.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Four additional goodness-of-fit plots are presented side by side for comparison.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.multiple.plot.default for details.

Value

Returns a compound plot comprising plots of weighted population residuals (WRES) vs population predictions (PRED), absolute individual weighted residuals (|IWRES|) vs independent variable (IDV), WRES vs IDV, and weighted population residuals vs log(IDV).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
addit.gof(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
addit.gof(xpdb)

Autocorrelation of conditional weighted residuals for Xpose 4

Description

This is an autocorrelation plot of conditional weighted residuals, a specific function in Xpose 4. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

autocorr.cwres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)
autocorr.cwres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see `plot`: '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`smooth`	Logical value indicating whether a smooth should be superimposed.
`ids`	A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` xpose data variable).
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

Value

Returns an autocorrelation plot for conditional weighted population residuals (CWRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
autocorr.cwres(xpdb)

## A conditioning plot
autocorr.cwres(xpdb, dilution=TRUE)

## Custom heading and axis labels
autocorr.cwres(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
autocorr.cwres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
autocorr.cwres(xpdb)

## A conditioning plot
autocorr.cwres(xpdb, dilution=TRUE)

## Custom heading and axis labels
autocorr.cwres(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
autocorr.cwres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

autocorrelation of the individual weighted residuals

Description

autocorrelation of the individual weighted residuals

Usage

autocorr.iwres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)
autocorr.iwres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)

Arguments

`object`	An "xpose.data" object.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`ids`	A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` xpose data variable).
`main`	A string giving the plot title or `NULL` if none.
`...`	Other arguments passed to `xpose.panel.default`.

Value

A Lattice object

Autocorrelation of weighted residuals for Xpose 4

Description

This is an autocorrelation plot of weighted residuals. Most of the options take their default values from the xpose.data object but may be overridden by supplying them as arguments.

Usage

autocorr.wres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)
autocorr.wres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see `plot`: '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`smooth`	Logical value indicating whether a smooth should be superimposed.
`ids`	A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` xpose data variable).
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns an autocorrelation plot for weighted population residuals (WRES) or individual weighted residuals (IWRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
autocorr.wres(xpdb)

## A conditioning plot
autocorr.wres(xpdb, dilution=TRUE)

## Custom heading and axis labels
autocorr.wres(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
autocorr.wres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## A vanilla plot with IWRES
autocorr.iwres(xpdb)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
autocorr.wres(xpdb)

## A conditioning plot
autocorr.wres(xpdb, dilution=TRUE)

## Custom heading and axis labels
autocorr.wres(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
autocorr.wres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## A vanilla plot with IWRES
autocorr.iwres(xpdb)

Basic goodness-of-fit plots, for Xpose 4

Description

This is a compound plot consisting of plots of observations (DV) vs population predictions (PRED), observations (DV) vs individual predictions (IPRED), absolute individual weighted residuals (|IWRES|) vs IPRED, and weighted population residuals (CWRES) vs independent variable (IDV), a specific function in Xpose 4. WRES are also supported. It is a wrapper encapsulating arguments to the dv.vs.pred, dv.vs.ipred, absval.iwres.vs.ipred and wres.vs.idv functions.

Usage

basic.gof(object, force.wres = FALSE, main = "Default", use.log = FALSE, ...)
basic.gof(object, force.wres = FALSE, main = "Default", use.log = FALSE, ...)

Arguments

`object`	An xpose.data object.
`force.wres`	Should the plots use WRES? Values can be `TRUE/FALSE`. Otherwise the CWRES are used if present.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`use.log`	Should we use log transformations in the plots?
`...`	Other arguments passed to `xpose.plot.default`.

Details

Four basic goodness-of-fit plots are presented side by side for comparison.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

basic.gof.cwres is just a wrapper for basic.gof with use.cwres=TRUE.

Value

Returns a compound plot comprising plots of observations (DV) vs population predictions (PRED), DV vs individual predictions (IPRED), absolute individual weighted residuals (|IWRES|) vs IPRED, and weighted populations residuals (WRES) vs the independent variable (IDV).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


basic.gof(simpraz.xpdb)

basic.gof(simpraz.xpdb)

Basic model comparison plots, for Xpose 4

Description

This creates a stack of four plots, comparing PRED, IPRED, WRES (or CWRES), and IWRES estimates for the two specified model fits.

Usage

basic.model.comp(
  object,
  object.ref = NULL,
  onlyfirst = FALSE,
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  main = "Default",
  force.wres = FALSE,
  ...
)
basic.model.comp(
  object,
  object.ref = NULL,
  onlyfirst = FALSE,
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  main = "Default",
  force.wres = FALSE,
  ...
)

Arguments

`object`	An xpose.data object.
`object.ref`	An xpose.data object. If not supplied, the user will be prompted.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot. The default is TRUE.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`force.wres`	Force function to use WRES?
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Four basic model comparison plots are displayed in sequence.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of plots comprising comparisons of PRED, IPRED, WRES (or CWRES) and IWRES for the two specified runs.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)

## A vanilla plot, without prompts
basic.model.comp(xpdb5, xpdb6, prompt = FALSE)

## Custom colours and symbols, no user IDs
basic.model.comp.cwres(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)

## End(Not run)


## Not run: 
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)

## A vanilla plot, without prompts
basic.model.comp(xpdb5, xpdb6, prompt = FALSE)

## Custom colours and symbols, no user IDs
basic.model.comp.cwres(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)

## End(Not run)

Function to create histograms of results from the `bootstrap` tool in PsN

Description

Reads results from the bootstrap tool in PsN and then creates histograms.

Usage

boot.hist(
  results.file = "raw_results_run1.csv",
  incl.ids.file = "included_individuals1.csv",
  min.failed = FALSE,
  cov.failed = FALSE,
  cov.warnings = FALSE,
  boundary = FALSE,
  showOriginal = TRUE,
  showMean = FALSE,
  showMedian = FALSE,
  showPCTS = FALSE,
  PCTS = c(0.025, 0.975),
  excl.id = c(),
  layout = NULL,
  sort.plots = TRUE,
  main = "Default",
  ...
)
boot.hist(
  results.file = "raw_results_run1.csv",
  incl.ids.file = "included_individuals1.csv",
  min.failed = FALSE,
  cov.failed = FALSE,
  cov.warnings = FALSE,
  boundary = FALSE,
  showOriginal = TRUE,
  showMean = FALSE,
  showMedian = FALSE,
  showPCTS = FALSE,
  PCTS = c(0.025, 0.975),
  excl.id = c(),
  layout = NULL,
  sort.plots = TRUE,
  main = "Default",
  ...
)

Arguments

`results.file`	The location of the results file from the `bootstrap` tool in PsN
`incl.ids.file`	The location of the included ids file from the `bootstrap` tool in PsN
`min.failed`	Should NONMEM runs that had failed minimization be skipped? `TRUE` or `FALSE`
`cov.failed`	Should NONMEM runs that had a failed covariance step be skipped? `TRUE` or `FALSE`
`cov.warnings`	Should NONMEM runs that had covariance step warnings be skipped? `TRUE` or `FALSE`
`boundary`	Should NONMEM runs that had boundary warnings be skipped? `TRUE` or `FALSE`
`showOriginal`	Should we show the value from the original NONMEM run in the histograms? `TRUE` or `FALSE`
`showMean`	Should we show the mean of the histogram data? `TRUE` or `FALSE`
`showMedian`	Should we show the median of the histogram data? `TRUE` or `FALSE`
`showPCTS`	Should we show the percentiles of the histogram data? `TRUE` or `FALSE`
`PCTS`	the percentiles to show. Can be a vector of any length. For example, `c(0.05,0.2,0.5,0.7)`
`excl.id`	Vector of id numbers to exclude.
`layout`	Layout of plots. A vector of number of rows and columns in each plot. `c(3,3)` for example.
`sort.plots`	Should the plots be sorted based on type of parameter. Sorting on parameters, standard errors, shrinkage and eigenvalues.
`main`	The title of the plot.
`...`	Additional arguments that can be passed to xpose.plot.histogram, xpose.panel.histogram, histogram and other lattice-package functions.

Value

A lattice object

Author(s)

Andrew Hooker

References

PsN

Examples


## Not run: 
boot.hist(results.file="./boot1/raw_results_run1.csv", 
          incl.ids.file="./boot1/included_individuals1.csv")

## End(Not run)

## Not run: 
boot.hist(results.file="./boot1/raw_results_run1.csv", 
          incl.ids.file="./boot1/included_individuals1.csv")

## End(Not run)

Print summary information for a bootgam or bootscm

Description

This functions prints some summary information for a bootgam performed in Xpose, or for a bootscm performed in PsN.

Usage

bootgam.print(bootgam.obj = NULL)
bootgam.print(bootgam.obj = NULL)

Arguments

bootgam.obj

The bootgam or bootscm object.

Value

No value returned

Author(s)

Ron Keizer

Examples


## Not run: 
bootgam.print(current.bootgam)  # Print summary for the current Xpose bootgam object
bootgam.print(current.bootscm)  # Print summary for the current Xpose bootscm object

## End(Not run)

## Not run: 
bootgam.print(current.bootgam)  # Print summary for the current Xpose bootgam object
bootgam.print(current.bootscm)  # Print summary for the current Xpose bootscm object

## End(Not run)

Import bootscm data into R/Xpose

Description

This function imports data generated by the PsN boot_scm function into the Xpose / R environment.

Usage

bootscm.import(
  scm.folder = NULL,
  silent = FALSE,
  n.bs = NULL,
  cov.recoding = NULL,
  group.by.cov = NULL,
  skip.par.est.import = FALSE,
  dofv.forward = 3.84,
  dofv.backward = 6.64,
  runno = NULL,
  return.obj = FALSE
)
bootscm.import(
  scm.folder = NULL,
  silent = FALSE,
  n.bs = NULL,
  cov.recoding = NULL,
  group.by.cov = NULL,
  skip.par.est.import = FALSE,
  dofv.forward = 3.84,
  dofv.backward = 6.64,
  runno = NULL,
  return.obj = FALSE
)

Arguments

`scm.folder`	The folder in which the PsN-generated bootscm data are.
`silent`	Don't output any progress report. Default is FALSE.
`n.bs`	The number of bootstraps performed. Defaults to 100.
`cov.recoding`	For categorical covariates that are recoded to dichotomous covariates within the bootscm configuration file, a list can be specified containing data frames for recoding. See the example below for details.
`group.by.cov`	Group inclusion frequencies by covariate, instead of calculating them per parameter-covariates relationship. Default is NULL, which means that the user will be asked to make a choice.
`skip.par.est.import`	Skip the import of all parameter estimates (in each final model in all scm's, as well as parameter estimates in first step of each scm). These data are required to make plot that show inclusion bias and correlation in parameter estimates. Importing these data takes a bit of time (may take a minute or so), so if you don't intend to make these plots anyhow this step can be skipped. Default is FALSE.
`dofv.forward`	dOFV value used in forward step of scm.
`dofv.backward`	dOFV value used in backward step of scm.
`runno`	The run-number of the base model for this bootSCM.
`return.obj`	Should the bootscm object be returned by the function?

Author(s)

Ron Keizer

Categorical observations vs. independent variable using stacked bars.

Description

Categorical observations vs. independent variable using stacked bars.

Usage

cat.dv.vs.idv.sb(
  object,
  dv = xvardef("dv", object),
  idv = xvardef("idv", object),
  by = NULL,
  groups = dv,
  force.by.factor = FALSE,
  recur = F,
  xlb = idv,
  ylb = "Proportion",
  subset = NULL,
  vary.width = T,
  level.to.plot = NULL,
  refactor.levels = TRUE,
  main = xpose.create.title.text(idv, dv, "Proportions of", object, subset = subset, ...),
  stack = TRUE,
  horizontal = FALSE,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  scales = list(),
  inclZeroWRES = TRUE,
  onlyfirst = FALSE,
  samp = NULL,
  aspect = object@[email protected]$aspect,
  auto.key = "Default",
  mirror = FALSE,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)
cat.dv.vs.idv.sb(
  object,
  dv = xvardef("dv", object),
  idv = xvardef("idv", object),
  by = NULL,
  groups = dv,
  force.by.factor = FALSE,
  recur = F,
  xlb = idv,
  ylb = "Proportion",
  subset = NULL,
  vary.width = T,
  level.to.plot = NULL,
  refactor.levels = TRUE,
  main = xpose.create.title.text(idv, dv, "Proportions of", object, subset = subset, ...),
  stack = TRUE,
  horizontal = FALSE,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  scales = list(),
  inclZeroWRES = TRUE,
  onlyfirst = FALSE,
  samp = NULL,
  aspect = object@Prefs@Graph.prefs$aspect,
  auto.key = "Default",
  mirror = FALSE,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)

Arguments

`object`	Xpose data object.
`dv`	The dependent variable (e.g. `"DV"` or `"CP"`.)
`idv`	The independent variable (e.g. `"TIME"`.)
`by`	Conditioning variable
`groups`	How we should group values in each conditional plot.
`force.by.factor`	Should we force the data to be treated as factors?
`recur`	Not used.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`subset`	Subset of data.
`vary.width`	Should we vary the width of the bars to match amount of information?
`level.to.plot`	Which levels of the DV to plot.
`refactor.levels`	Should we refactor the levels?
`main`	The title of the plot.
`stack`	Should we stack the bars?
`horizontal`	Should the bars be horizontal?
`strip`	Defining how the strips should appear in the conditioning plots.
`scales`	Scales argument to `xyplot`.
`inclZeroWRES`	Include rows with WRES=0?
`onlyfirst`	Only include first data point for each individual?
`samp`	Sample to use in mirror plot (a number).
`aspect`	Aspect argument to `xyplot`.
`auto.key`	Make a legend.
`mirror`	Mirror can be `FALSE`, `TRUE`, 1 or 3.
`mirror.aspect`	Aspect for mirror.
`pass.plot.list`	Should the plot list be passed back to user?
`x.cex`	Size of x axis label.
`y.cex`	Size of Y axis label.
`main.cex`	Size of Title.
`mirror.internal`	Internal stuff.
`...`	Other arguments passed to function.

Author(s)

Andrew Hooker

Examples


## Not run: 
## read in table files
runno <- 45
xpdb <- xpose.data(runno)

## make some stacked bar plots
cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F)
cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by="DOSE")
cat.dv.vs.idv.sb(xpdb,idv="DOSE")
cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by="TIME")
cat.dv.vs.idv.sb(xpdb,idv="TIME")
cat.dv.vs.idv.sb(xpdb,idv="CAVH")
cat.dv.vs.idv.sb(xpdb,idv="TIME",by="DOSE",scales=list(x=list(rot=45)))

## make some mirror plots
cat.dv.vs.idv.sb(xpdb,idv="DOSE",mirror=1)
cat.dv.vs.idv.sb(xpdb,idv="CAVH",mirror=1,auto.key=F)

## End(Not run)

## Not run: 
## read in table files
runno <- 45
xpdb <- xpose.data(runno)

## make some stacked bar plots
cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F)
cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by="DOSE")
cat.dv.vs.idv.sb(xpdb,idv="DOSE")
cat.dv.vs.idv.sb(xpdb,idv=NULL,stack=F,by="TIME")
cat.dv.vs.idv.sb(xpdb,idv="TIME")
cat.dv.vs.idv.sb(xpdb,idv="CAVH")
cat.dv.vs.idv.sb(xpdb,idv="TIME",by="DOSE",scales=list(x=list(rot=45)))

## make some mirror plots
cat.dv.vs.idv.sb(xpdb,idv="DOSE",mirror=1)
cat.dv.vs.idv.sb(xpdb,idv="CAVH",mirror=1,auto.key=F)

## End(Not run)

Categorical (visual) predictive check.

Description

Categorical (visual) predictive check plots.

Usage

cat.pc(
  object,
  dv = xvardef("dv", object),
  idv = xvardef("idv", object),
  level.to.plot = NULL,
  subset = NULL,
  histo = T,
  median.line = F,
  PI.lines = F,
  xlb = if (histo) {
     paste("Proportion of ", dv)
 } else {
     paste(idv)
 },
  ylb = if (histo) {
     paste("Percent of Total")
 } else {
    
    paste("Proportion of Total")
 },
  main = xpose.create.title.text(NULL, dv, "Predictive check of", object, subset =
    subset, ...),
  strip = "Default",
  ...
)
cat.pc(
  object,
  dv = xvardef("dv", object),
  idv = xvardef("idv", object),
  level.to.plot = NULL,
  subset = NULL,
  histo = T,
  median.line = F,
  PI.lines = F,
  xlb = if (histo) {
     paste("Proportion of ", dv)
 } else {
     paste(idv)
 },
  ylb = if (histo) {
     paste("Percent of Total")
 } else {
    
    paste("Proportion of Total")
 },
  main = xpose.create.title.text(NULL, dv, "Predictive check of", object, subset =
    subset, ...),
  strip = "Default",
  ...
)

Arguments

`object`	Xpose data object.
`dv`	The dependent variable (e.g. `"DV"` or `"CP"`.)
`idv`	The independent variable (e.g. `"TIME"`.)
`level.to.plot`	The levels to plot.
`subset`	Subset of data.
`histo`	If `FALSE` then a VPC is created, given that `idv` is defined.
`median.line`	Make a median line?
`PI.lines`	Make prediction interval lines?
`xlb`	Label for x axis.
`ylb`	label for y axis.
`main`	Main title.
`strip`	Defining how the strips should appear in the conditioning plots.
`...`	Extra arguments passed to the function.

Author(s)

Andrew C. Hooker

Examples


## Not run: 
## read in table files
runno <- 45
xpdb <- xpose.data(runno)

## create proportion (visual) predictive check
cat.pc(xpdb,idv=NULL)
cat.pc(xpdb,idv="DOSE")
cat.pc(xpdb,idv="DOSE",histo=F)
cat.pc(xpdb,idv="TIME",histo=T,level.to.plot=1)

## End(Not run)

## Not run: 
## read in table files
runno <- 45
xpdb <- xpose.data(runno)

## create proportion (visual) predictive check
cat.pc(xpdb,idv=NULL)
cat.pc(xpdb,idv="DOSE")
cat.pc(xpdb,idv="DOSE",histo=F)
cat.pc(xpdb,idv="TIME",histo=T,level.to.plot=1)

## End(Not run)

Functions changing variable definitions in Xpose 4

Description

These functions allow customization of Xpose's graphics settings.

Usage

change.ab.graph.par(object, classic = FALSE)

change.bw.graph.par(object, classic = FALSE)

change.cond.graph.par(object, classic = FALSE)

change.dil.graph.par(object, classic = FALSE)

change.label.par(object, classic = FALSE)

change.lm.graph.par(object, classic = FALSE)

change.misc.graph.par(object, classic = FALSE)

change.pi.graph.par(object, classic = FALSE)

change.smooth.graph.par(object, classic = FALSE)
change.ab.graph.par(object, classic = FALSE)

change.bw.graph.par(object, classic = FALSE)

change.cond.graph.par(object, classic = FALSE)

change.dil.graph.par(object, classic = FALSE)

change.label.par(object, classic = FALSE)

change.lm.graph.par(object, classic = FALSE)

change.misc.graph.par(object, classic = FALSE)

change.pi.graph.par(object, classic = FALSE)

change.smooth.graph.par(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

Settings can be saved and loaded using export.graph.par and import.graph.par, respectively.

Value

An xpose.data object (classic == FALSE) or null (classic == TRUE).

Functions

change.ab.graph.par(): change settings for the line of identity.
change.bw.graph.par(): sets preferences for box-and-whisker plots
change.cond.graph.par(): sets preferences for conditioning
change.dil.graph.par(): responsible for dilution preferences
change.label.par(): responsible for labelling preferences
change.lm.graph.par(): responsible for linear regression lines.
change.misc.graph.par(): sets basic graphics parameters, including plot type, point type and size, colour, line type, and line width.
change.pi.graph.par(): responsible for prediction interval plotting preferences
change.smooth.graph.par(): sets preferences for loess smooths.

Author(s)

Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Change default miscellaneous graphic preferences
xpdb5 <- change.misc.graph.par(xpdb5)

## Change default linear regression line preferences, creating a new 
## object
xpdb5.a <- change.lm.graph.par(xpdb5)

## Change conditioning preferences
xpdb5 <- change.cond.graph.par(xpdb5)

## End(Not run)


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Change default miscellaneous graphic preferences
xpdb5 <- change.misc.graph.par(xpdb5)

## Change default linear regression line preferences, creating a new 
## object
xpdb5.a <- change.lm.graph.par(xpdb5)

## Change conditioning preferences
xpdb5 <- change.cond.graph.par(xpdb5)

## End(Not run)

Functions changing miscellaneous parameter settings in Xpose 4

Description

These functions allow viewing and changing of settings relating to subsets, categorical threshold values, documentation and numbers indicating missing data values.

Usage

change.cat.cont(
  object,
  listall = TRUE,
  classic = FALSE,
  to.cat.vec = NULL,
  to.cont.vec = NULL,
  change.type.vec = NULL,
  ...
)

change.cat.cont(
  object,
  listall = TRUE,
  classic = FALSE,
  to.cat.vec = NULL,
  to.cont.vec = NULL,
  ...
) <- value

change.cat.levels(object, classic = FALSE, cat.limit = NULL, ...)

change.cat.levels(object, classic = FALSE, ...) <- value

change.dv.cat.levels(object, classic = FALSE, dv.cat.limit = NULL, ...)

change.dv.cat.levels(object, classic = FALSE, ...) <- value

change.miss(object, classic = FALSE)

change.subset(object, classic = FALSE)

get.doc(object, classic = FALSE)

set.doc(object, classic = FALSE)
change.cat.cont(
  object,
  listall = TRUE,
  classic = FALSE,
  to.cat.vec = NULL,
  to.cont.vec = NULL,
  change.type.vec = NULL,
  ...
)

change.cat.cont(
  object,
  listall = TRUE,
  classic = FALSE,
  to.cat.vec = NULL,
  to.cont.vec = NULL,
  ...
) <- value

change.cat.levels(object, classic = FALSE, cat.limit = NULL, ...)

change.cat.levels(object, classic = FALSE, ...) <- value

change.dv.cat.levels(object, classic = FALSE, dv.cat.limit = NULL, ...)

change.dv.cat.levels(object, classic = FALSE, ...) <- value

change.miss(object, classic = FALSE)

change.subset(object, classic = FALSE)

get.doc(object, classic = FALSE)

set.doc(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`listall`	A logical operator specifying whether the items in the database should be listed.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.
`to.cat.vec`	A vector of strings specifying the names of the categorical variables that should be transformed to continuous.
`to.cont.vec`	A vector of strings specifying the names of the continuous variables that should be transformed to categorical.
`change.type.vec`	A vector of strings specifying the names of the variables that should be transformed to/from continuous/categorical.
`...`	arguments passed to other functions.
`value`	This is the value that will be replaced in the xpose data object `object`. `value` is used in the “replacement function” version of these functions. That is the form where we have `function.name(object) <- value`. If `value` is `NULL` then the functions prompt the user for a value. For `change.cat.levels`, `value` is the categorical limit `cat.limit`. For `change.dv.cat.levels`, `value` is the DV categorical limit `dv.cat.limit`. For `change.cat.cont`, `value` is the `change.type.vec`. See the examples below.
`cat.limit`	The limit for which we treat a list of values as categorical. If there are `cat.limit` or less unique values then the list is treated as categorical.
`dv.cat.limit`	The limit for which we treat DV as categorical. If there are `dv.cat.limit` or less unique dv values then dv is treated as categorical.

Value

An xpose.data object, except get.doc, which returns the value of object@Doc.

Functions

change.cat.cont(): allows interchange between categorical and continuous data formats within the Xpose database. This in turn affects how plots are drawn.
change.cat.cont( object, listall = TRUE, classic = FALSE, to.cat.vec = NULL, to.cont.vec = NULL, ... ) <- value: allows interchange between categorical and continuous data formats within the Xpose database. This in turn affects how plots are drawn.
change.cat.levels(): change settings for the number of unique data values required in a variable in order to define it as continuous for ordinary variables.
change.cat.levels(object, classic = FALSE, ...) <- value: change settings for the number of unique data values required in a variable in order to define it as continuous for ordinary variables.
change.dv.cat.levels(): change settings for the number of unique data values required in a variable in order to define it as continuous for the dependent variable.
change.dv.cat.levels(object, classic = FALSE, ...) <- value: change settings for the number of unique data values required in a variable in order to define it as continuous for the dependent variable.
change.miss(): change the value to use as 'missing'.
change.subset(): is used for setting the data item's subset field. To specify a subset of the data to process, you use the variable names and the regular R selection operators. To combine a subset over two or more variables, the selection expressions for the two variables are combined using R's unary logical operators.

The variable names are those that are specified in the NONMEM table files (e.g. PRED, TIME, SEX).

The selection operators are: == (equal) != (not equal) || (or) > (greater than) < (less than)

For example, to specify that TIME less than 24 should be processed, you type the expression: TIME < 24.

The unary logical operators are: & (and) | (or)

For example, to specify TIME less than 24 and males (SEX equal to 1), you type the expression: TIME < 24 & SEX == 1

This subset selection scheme works on all variables, including ID numbers.

The subset selection is not entirely stable. For example, there is no check that the user enters a valid expression, nor that the user specifies existing variable names. An erroneous expression will not become evident until a plot is attempted and the expression takes effect.
get.doc(): get the documentation field in the Xpose data object.
set.doc(): set the documentation field in the Xpose data object.

Author(s)

Andrew Hooker, Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Change default subset
xpdb5 <- change.subset(xpdb5)

## Set documentation field
xpdb5 <- set.doc(xpdb5)
## View it
view.doc(xpdb5)

## change the categorical limit for the dv variable
change.dv.cat.levels(xpdb5) <- 10

## change the categorical limit for non DV variables
change.cat.levels(xpdb5) <- 2
## or
xpdb5 <- change.cat.levels(xpdb5,cat.levels=2)

## chnage variables from categorical to continuous
xpdb5 <- change.cat.cont(xpdb5,to.cat.vec=c("AGE"),to.cont.vec=c("SEX"))
xpdb5 <- change.cat.cont(xpdb5,change.type.vec=c("AGE","SEX"))
change.cat.cont(xpdb5) <- c("AGE","SEX")

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Change default subset
xpdb5 <- change.subset(xpdb5)

## Set documentation field
xpdb5 <- set.doc(xpdb5)
## View it
view.doc(xpdb5)

## change the categorical limit for the dv variable
change.dv.cat.levels(xpdb5) <- 10

## change the categorical limit for non DV variables
change.cat.levels(xpdb5) <- 2
## or
xpdb5 <- change.cat.levels(xpdb5,cat.levels=2)

## chnage variables from categorical to continuous
xpdb5 <- change.cat.cont(xpdb5,to.cat.vec=c("AGE"),to.cont.vec=c("SEX"))
xpdb5 <- change.cat.cont(xpdb5,change.type.vec=c("AGE","SEX"))
change.cat.cont(xpdb5) <- c("AGE","SEX")

## End(Not run)

Change parameter scope.

Description

Function to change the parameter scope.

Usage

change.parm(object, listall = TRUE, classic = FALSE)
change.parm(object, listall = TRUE, classic = FALSE)

Arguments

`object`	The xpose data object.
`listall`	whether we should list all the current parameters.
`classic`	true if used in the classic menu system (for internal use).

Value

If classic then return nothing. Otherwise return the new data object.

Author(s)

Andrew C. Hooker

Changes the name of an Xpose data item

Description

This function allows the names of data items in the Xpose database to be changed.

Usage

change.var.name(object, classic = FALSE)
change.var.name(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

This function facilitates the changing of data item names in the object@Data slot.

Value

An xpose.data object.

Author(s)

Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpdb5 <- change.var.name(xpdb5)

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpdb5 <- change.var.name(xpdb5)

## End(Not run)

Changes the label of an Xpose data item

Description

This function allows the labels of data items in the Xpose database to be changed.

Usage

change.xlabel(object, listall = TRUE, classic = FALSE)
change.xlabel(object, listall = TRUE, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`listall`	A logical operator specifying whether the items in the database should be listed.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

This function facilitates the changing of data item labels in the object@Prefs@Labels slot.

Value

An xpose.data object.

Author(s)

Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpdb5 <- change.xlabel(xpdb5)

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpdb5 <- change.xlabel(xpdb5)

## End(Not run)

Change Xpose variable definitions.

Description

These functions allow for the changing of Xpose variable definitions like "idv" and "dv". These variable definitions are used to refer to columns of the observed data in a generic way, so that generic plotting functions can be created.

Usage

change.xvardef(
  object,
  var = ".ask",
  def = ".ask",
  listall = TRUE,
  classic = FALSE,
  check.var = FALSE,
  ...
)

change.xvardef(
  object,
  var,
  listall = FALSE,
  classic = FALSE,
  check.var = FALSE,
  ...
) <- value
change.xvardef(
  object,
  var = ".ask",
  def = ".ask",
  listall = TRUE,
  classic = FALSE,
  check.var = FALSE,
  ...
)

change.xvardef(
  object,
  var,
  listall = FALSE,
  classic = FALSE,
  check.var = FALSE,
  ...
) <- value

Arguments

`object`	An `xpose.data` object.
`var`	The Xpose variable you would like to change or add to the current object. A one-element character vector (e.g. `"idv"`). If `".ask"` the user will be prompted to input a value.
`def`	A vector of column names from NONMEM table files (`names(object@Data)`) that should be associated with this variable (e.g. `c("TIME")`). Multiple values are allowed. If `".ask"` the user will be prompted to input values.
`listall`	Should the function list the database values?
`classic`	Is the function being used from the classic interface. This is an internal option.
`check.var`	Should the variables be checked against the current variables in the object?
`...`	Items passed to functions within this function.
`value`	a vector of values

Value

If called from the the command line then this function returns an xpose database. If called from the classic interface this function updates the current xpose database (.cur.db).

Functions

change.xvardef( object, var, listall = FALSE, classic = FALSE, check.var = FALSE, ... ) <- value: Change the covariate scope of the xpose database object

Additional arguments

The default xpose variables are:

id: Individual identifier column in dataset
idlab: values used for plotting ID values on data points in plots
occ: The occasion variable
dv: The dv variable
pred: The pred variable
ipred: The ipred variable
wres: The wres variable
cwres: The cwres variable
res: The res variable
parms: The parameters in the database
covariates: The covariates in the database
ranpar: The random parameters in the database

Author(s)

Andrew Hooker

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

# Change the "id" variable to point to "PRED" in the xpose object
xpdb <- change.xvardef(xpdb,var="id",def="PRED")

# Check the value of the "id" variable
xvardef("id",xpdb)

# Change the "idv" variable
change.xvardef(xpdb,var="idv") <- "TIME"

# Change the covariate scope
change.xvardef(xpdb,var="covariates") <- c("SEX","AGE","WT")

## Not run: 
# Use the interactive capabilities of the function
xpdb <- change.xvardef(xpdb)

## End(Not run)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

# Change the "id" variable to point to "PRED" in the xpose object
xpdb <- change.xvardef(xpdb,var="id",def="PRED")

# Check the value of the "id" variable
xvardef("id",xpdb)

# Change the "idv" variable
change.xvardef(xpdb,var="idv") <- "TIME"

# Change the covariate scope
change.xvardef(xpdb,var="covariates") <- c("SEX","AGE","WT")

## Not run: 
# Use the interactive capabilities of the function
xpdb <- change.xvardef(xpdb)

## End(Not run)

Compute the Conditional Weighted Residuals

Description

This function computes the conditional weighted residuals (CWRES) from a NONMEM run. CWRES are an extension of the weighted residuals (WRES), but are calculated based on the first-order with conditional estimation (FOCE) method of linearizing a pharmacometric model (WRES are calculated based on the first-order (FO) method). The function requires a NONMEM table file and an extra output file that must be explicitly asked for when running NONMEM, see details below.

Usage

compute.cwres(
  run.number,
  tab.prefix = "cwtab",
  sim.suffix = "",
  est.tab.suffix = ".est",
  deriv.tab.suffix = ".deriv",
  old.file.convention = FALSE,
  id = "ALL",
  printToOutfile = TRUE,
  onlyNonZero = TRUE,
  ...
)

xpose.calculate.cwres(
  object,
  cwres.table.prefix = "cwtab",
  tab.suffix = "",
  sim.suffix = "sim",
  est.tab.suffix = ".est",
  deriv.tab.suffix = ".deriv",
  old.file.convention = FALSE,
  id = "ALL",
  printToOutfile = TRUE,
  onlyNonZero = FALSE,
  classic = FALSE,
  ...
)
compute.cwres(
  run.number,
  tab.prefix = "cwtab",
  sim.suffix = "",
  est.tab.suffix = ".est",
  deriv.tab.suffix = ".deriv",
  old.file.convention = FALSE,
  id = "ALL",
  printToOutfile = TRUE,
  onlyNonZero = TRUE,
  ...
)

xpose.calculate.cwres(
  object,
  cwres.table.prefix = "cwtab",
  tab.suffix = "",
  sim.suffix = "sim",
  est.tab.suffix = ".est",
  deriv.tab.suffix = ".deriv",
  old.file.convention = FALSE,
  id = "ALL",
  printToOutfile = TRUE,
  onlyNonZero = FALSE,
  classic = FALSE,
  ...
)

Arguments

`run.number`	The run number of the NONMEM from which the CWRES are to be calculated.
`tab.prefix`	The prefix to two NONMEM file containing the needed values for the computation of the CWRES, described in the details section.
`sim.suffix`	The suffix ,before the ".", of the NONMEM file containing the needed values for the computation of the CWRES, described in the details section. For example, the table files might be named `cwtab1sim.est` and `cwtab1sim.deriv`, in which case `sim.suffix="sim"`.
`est.tab.suffix`	The suffix, after the ".", of the NONMEM file containing the estimated parameter values needed for the CWRES calculation.
`deriv.tab.suffix`	The suffix, after the ".", of the NONMEM file containing the derivatives of the model with respect to the random parameters needed for the CWRES calculation.
`old.file.convention`	For backwards compatibility. Use this if you are using the previous file convention for CWRES (table files named cwtab1, cwtab1.50, cwtab1.51, ... , cwtab.58 for example).
`id`	Can be either "ALL" or a number matching an ID label in the `datasetname`. Value is fixed to "ALL" for `xpose.calculate.cwres`.
`printToOutfile`	Logical (TRUE/FALSE) indicating whether the CWRES values calculated should be appended to a copy of the `datasetname`. Only works if `id`="ALL". If chosen the resulting output file will be `datasetname`.cwres. Value is fixed to TRUE for `xpose.calculate.cwres`.
`onlyNonZero`	Logical (TRUE/FALSE) indicating if the return value (the CWRES values) of `compute.cwres` should include the zero values associated with non-measurement lines in a NONMEM data file.
`...`	Other arguments passed to basic functions in code.
`object`	An xpose.data object.
`cwres.table.prefix`	The prefix to the NONMEM table file containing the derivative of the model with respect to the etas and epsilons, described in the details section.
`tab.suffix`	The suffix to the NONMEM table file containing the derivative of the model with respect to the etas and epsilons, described in the details section.
`classic`	Indicates if the function is to be used in the classic menu system.

Details

The function reads in the following two files:

paste(tab.prefix,run.number,sim.suffix,est.tab.suffix,sep="")

paste(tab.prefix,run.number,sim.suffix,deriv.tab.suffix,sep="")

Which might be for example:

 cwtab1.est cwtab1.deriv

and (depending on the input values to the function) returns the CWRES in vector form as well as creating a new table file named:

paste(tab.prefix,run.number,sim.suffix,sep="")

Which might be for example:

 cwtab1

Value

compute.cwres: Returns a vector containing the values of the CWRES.
xpose.calculate.cwres: Returns an Xpose data object that contains the CWRES. If simulated data is present, then the CWRES will also be calculated for that data.

Functions

xpose.calculate.cwres(): This function is a wrapper around the function compute.cwres. It computes the CWRES for the model file associated with the Xpose data object input to the function. If possible it also computes the CWRES for any simulated data associated with the current Xpose data object. If you have problems with this function try using compute.cwres and then rereading your dataset into Xpose.

Setting up the NONMEM model file

In order for this function to calculate the CWRES, NONMEM must be run while requesting certain tables and files to be created. How these files are created differs depending on if you are using $PRED or ADVAN as well as the version of NONMEM you are using. These procedures are known to work for NONMEM VI but may be different for NONMEM V and NONMEM VII. We have attempted to indicate where NONMEM V may be different, but this has not been extensively tested! For NONMEM VII the CWRES are calculated internally so this function is rarely needed.

This procedure can be done automatically using Perl Speaks NONMEM (PsN) and we highly recommend using PsN for this purpose. After installing PsN just type 'execute [modelname] -cwres'. See https://uupharmacometrics.github.io/PsN/ for more details.

There are five main insertions needed in your NONMEM control file:

$ABB COMRES=X.

Insert this line directly after your $DATA line. The value of X is the number of ETA() terms plus the number of EPS() terms in your model. For example for a model with three ETA() terms and two EPS() terms the code would look like this:
```
$DATA temp.csv IGNORE=@ 
$ABB COMRES=5 
$INPUT ID TIME DV MDV AMT EVID 
$SUB ADVAN2 TRANS2
```
Verbatim code.
- Using ADVAN.
  
  If you are using ADVAN routines in your model, then Verbatim code should be inserted directly after the $ERROR section of your model file. The length of the code depends again on the number of ETA() terms and EPS() terms in your model. For each ETA(y) in your model there is a corresponding term G(y,1) that you must assign to a COM() variable. For each EPS(y) in your model, there is a corresponding HH(y,1) term that you must assign to a COM() variable.
  
  For example for a model using ADVAN routines with three ETA() terms and two EPS() terms the code would look like this:
```
"LAST 
"  COM(1)=G(1,1) 
"  COM(2)=G(2,1) 
"  COM(3)=G(3,1) 
"  COM(4)=HH(1,1) 
"  COM(5)=HH(2,1) 
```
- Using PRED.
  
  If you are using $PRED, the verbatim code should be inserted directly after the $PRED section of your model file. For each ETA(y) in your model there is a corresponding term G(y,1) that you must assign to a COM() variable. For each EPS(y) in your model, there is a corresponding H(y,1) term that you must assign to a COM() variable. The code would look like this for three ETA() terms and two EPS() terms:
```
"LAST 
"  COM(1)=G(1,1) 
"  COM(2)=G(2,1) 
"  COM(3)=G(3,1) 
"  COM(4)=H(1,1) 
"  COM(5)=H(2,1) 
```

INFN routine.

Using ADVAN with NONMEM VI and higher.

If you are using ADVAN routines in your model, then an $INFN section should be placed directly after the $PK section using the following code. In this example we are assuming that the model file is named something like 'run1.mod', thus the prefix to these file names 'cwtab' has the same run number attached to it (i.e. 'cwtab1'). This should be changed for each new run number.

$INFN
IF (ICALL.EQ.3) THEN
  OPEN(50,FILE='cwtab1.est')
  WRITE(50,*) 'ETAS'
  DO WHILE(DATA)                                                       
    IF (NEWIND.LE.1) WRITE (50,*) ETA                                    
  ENDDO                                                                
  WRITE(50,*) 'THETAS'
  WRITE(50,*) THETA
  WRITE(50,*) 'OMEGAS'
  WRITE(50,*) OMEGA(BLOCK)
  WRITE(50,*) 'SIGMAS'
  WRITE(50,*) SIGMA(BLOCK)
ENDIF

Using ADVAN with NONMEM V.

If you are using ADVAN routines in your model, then you need to use an INFN subroutine. If we call the INFN subroutine 'myinfn.for' then the $SUBS line of your model file should include the INFN option. That is, if we are using ADVAN2 and TRANS2 in our model file then the $SUBS line would look like:

$SUB ADVAN2 TRANS2 INFN=myinfn.for

The 'myinfn.for' routine for 4 thetas, 3 etas and 1 epsilon is shown below. If your model has different numbers of thetas, etas and epsilons then the values of NTH, NETA, and NEPS, should be changed respectively. These vales are found in the DATA statement of the subroutine. additionally, in this example we are assuming that the model file is named something like 'run1.mod', thus the prefix to the output file names ('cwtab') in this subroutine has the same run number attached to it (i.e. 'cwtab1'). This number should be changed for each new run number (see the line beginning with 'OPEN').

     SUBROUTINE INFN(ICALL,THETA,DATREC,INDXS,NEWIND)
     DIMENSION THETA(*),DATREC(*),INDXS(*)
     DOUBLE PRECISION THETA
     COMMON /ROCM6/ THETAF(40),OMEGAF(30,30),SIGMAF(30,30)
     COMMON /ROCM7/ SETH(40),SEOM(30,30),SESIG(30,30)
     COMMON /ROCM8/ OBJECT
     COMMON /ROCM9/ IERE,IERC
     DOUBLE PRECISION THETAF, OMEGAF, SIGMAF
     DOUBLE PRECISION OBJECT
     REAL SETH,SEOM,SESIG
     DOUBLE PRECISION ETA(10)
     INTEGER J,I
     INTEGER IERE,IERC
     INTEGER MODE
     INTEGER NTH,NETA,NEPS
     DATA NTH,NETA,NEPS/4,3,1/
     IF (ICALL.EQ.0) THEN
C      open files here, if necessary
       OPEN(50,FILE='cwtab1.est')
     ENDIF
     IF (ICALL.EQ.3) THEN
       MODE=0
       CALL PASS(MODE)
       MODE=1
       WRITE(50,*) 'ETAS'
20     CALL PASS(MODE)
       IF (MODE.EQ.0) GO TO 30
       IF (NEWIND.NE.2) THEN
         CALL GETETA(ETA)
         WRITE (50,97) (ETA(I),I=1,NETA)
       ENDIF
       GO TO 20
30     CONTINUE
       WRITE (50,*) 'THETAS'
       WRITE (50,99) (THETAF(J),J=1,NTH)
       WRITE(50,*) 'OMEGAS'
       DO 7000 I=1,NETA
7000     WRITE (50,99) (OMEGAF(I,J),J=1,NETA)
       WRITE(50,*) 'SIGMAS'
       DO 7999 I=1,NEPS
7999     WRITE (50,99) (SIGMAF(I,J),J=1,NEPS)
     ENDIF
99   FORMAT (20E15.7)
98   FORMAT (2I8)
97   FORMAT (10E15.7)
     RETURN
     END

Using $PRED with NONMEM VI and higher.

If you are using $PRED, then an the following code should be placed at the end of the $PRED section of the model file (together with the verbatim code). In this example we are assuming that the model file is named something like 'run1.mod', thus the prefix to these file names 'cwtab' has the same run number attached to it (i.e. 'cwtab1'). This should be changed for each new run number.

IF (ICALL.EQ.3) THEN
  OPEN(50,FILE='cwtab1.est')
  WRITE(50,*) 'ETAS'
  DO WHILE(DATA)                                                       
    IF (NEWIND.LE.1) WRITE (50,*) ETA                                    
  ENDDO                                                                
  WRITE(50,*) 'THETAS'
  WRITE(50,*) THETA
  WRITE(50,*) 'OMEGAS'
  WRITE(50,*) OMEGA(BLOCK)
  WRITE(50,*) 'SIGMAS'
  WRITE(50,*) SIGMA(BLOCK)
ENDIF

Using $PRED with NONMEM V.

If you are using $PRED with NONMEM V, then you need to add verbatim code immediately after the $PRED command. In this example we assume 4 thetas, 3 etas and 1 epsilon. If your model has different numbers of thetas, etas and epsilons then the values of NTH, NETA, and NEPS, should be changed respectively. These vales are found in the DATA statement below.

$PRED
"FIRST  
"     COMMON /ROCM6/ THETAF(40),OMEGAF(30,30),SIGMAF(30,30) 
"     COMMON /ROCM7/ SETH(40),SEOM(30,30),SESIG(30,30) 
"     COMMON /ROCM8/ OBJECT 
"     DOUBLE PRECISION THETAF, OMEGAF, SIGMAF 
"     DOUBLE PRECISION OBJECT 
"     REAL SETH,SEOM,SESIG 
"     INTEGER J,I 
"     INTEGER MODE 
"     INTEGER NTH,NETA,NEPS 
"     DATA NTH,NETA,NEPS/4,3,1/

After this verbatim code you add all of the abbreviated code needed for the $PRED routine in your model file. After the abbreviated code more verbatim code is needed. This verbatim code should be added before the verbatim code discussed above under point 2. In the example below we are assuming that the model file is named something like 'run1.mod', thus the prefix to the output file names ('cwtab') has the same run number attached to it (i.e. 'cwtab1'). This number should be changed for each new run number (see the line beginning with 'OPEN').

"     IF (ICALL.EQ.0) THEN 
"C    open files here, if necessary 
"       OPEN(50,FILE='cwtab1.est') 
"     ENDIF 
"     IF (ICALL.EQ.3) THEN 
"       MODE=0 
"       CALL PASS(MODE) 
"       MODE=1 
" 	     WRITE(50,*) 'ETAS' 
"20     CALL PASS(MODE) 
"       IF (MODE.EQ.0) GO TO 30 
"       IF (NEWIND.NE.2) THEN 
"         CALL GETETA(ETA) 
"         WRITE (50,97) (ETA(I),I=1,NETA) 
"       ENDIF 
"       GO TO 20 
"30     CONTINUE 
"       WRITE (50,*) 'THETAS' 
"       WRITE (50,99) (THETAF(J),J=1,NTH) 
"       WRITE (50,*) 'OMEGAS' 
"       DO 7000 I=1,NETA 
"7000     WRITE (50,99) (OMEGAF(I,J),J=1,NETA) 
"       WRITE (50,*) 'SIGMAS' 
"       DO 7999 I=1,NEPS 
"7999     WRITE (50,99) (SIGMAF(I,J),J=1,NEPS) 
"     ENDIF 
"99   FORMAT (20E15.7) 
"98   FORMAT (2I8) 
"97   FORMAT (10E15.7)

cwtab*.deriv table file.

A special table file needs to be created to print out the values contained in the COMRES variables. In addition the ID, IPRED, MDV, DV, PRED and RES data items are needed for the computation of the CWRES. The following code should be added to the NONMEM model file. In this example we continue to assume that we are using a model with three ETA() terms and two EPS() terms, extra terms should be added for new ETA() and EPS() terms in the model file. We also assume the model file is named something like 'run1.mod', thus the prefix to these file names 'cwtab' has the same run number attached to it (i.e. 'cwtab1'). This should be changed for each new run number.
```
$TABLE ID COM(1)=G11 COM(2)=G21 COM(3)=G31 COM(4)=H11 COM(5)=H21 
       IPRED MDV NOPRINT ONEHEADER FILE=cwtab1.deriv 
```
$ESTIMATION.

To compute the CWRES, the NONMEM model file must use (at least) the FO method with the POSTHOC step. If the FO method is used and the POSTHOC step is not included then the CWRES values will be equivalent to the WRES. The CWRES calculations are based on the FOCE approximation, and consequently give an idea of the ability of the FOCE method to fit the model to the data. If you are using another method of parameter estimation (e.g. FOCE with interaction), the CWRES will not be calculated based on the same model linearization procedure.

Author(s)

Andrew Hooker

References

Hooker AC, Staatz CE, Karlsson MO. Conditional weighted residuals, an improved model diagnostic for the FO/FOCE methods. PAGE 15 (2006) Abstr 1001 [http://www.page-meeting.org/?abstract=1001].

Hooker AC, Staatz CE and Karlsson MO, Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method, Pharm Res, 24(12): p. 2187-97, 2007, [doi:10.1007/s11095-007-9361-x].

Examples


## Not run: 
## Capture CWRES from cwtab5.est and cwtab5.deriv
cwres <- compute.cwres(5)
mean(cwres)
var(cwres)

## Capture CWRES from cwtab1.est and cwtab1.deriv, do not print out, allow zeroes
cwres <- compute.cwres("1", printToOutFile = FALSE,
  onlyNonZero = FALSE)

## Capture CWRES for ID==1
cwres.1 <- compute.cwres("1", id=1)

## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Compare WRES, CWRES
xpdb5 <- xpose.calculate.cwres(xpdb5)
cwres.wres.vs.idv(xpdb5) 


## End(Not run)

## Not run: 
## Capture CWRES from cwtab5.est and cwtab5.deriv
cwres <- compute.cwres(5)
mean(cwres)
var(cwres)

## Capture CWRES from cwtab1.est and cwtab1.deriv, do not print out, allow zeroes
cwres <- compute.cwres("1", printToOutFile = FALSE,
  onlyNonZero = FALSE)

## Capture CWRES for ID==1
cwres.1 <- compute.cwres("1", id=1)

## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Compare WRES, CWRES
xpdb5 <- xpose.calculate.cwres(xpdb5)
cwres.wres.vs.idv(xpdb5) 


## End(Not run)

Plot scatterplot matrices of parameters, random parameters or covariates

Description

These functions plot scatterplot matrices of parameters, random parameters and covariates.

Usage

cov.splom(
  object,
  main = xpose.multiple.plot.title(object = object, plot.text =
    "Scatterplot matrix of covariates", ...),
  varnames = NULL,
  onlyfirst = TRUE,
  smooth = TRUE,
  lmline = NULL,
  ...
)

parm.splom(
  object,
  main = xpose.multiple.plot.title(object = object, plot.text =
    "Scatterplot matrix of parameters", ...),
  varnames = NULL,
  onlyfirst = TRUE,
  smooth = TRUE,
  lmline = NULL,
  ...
)

ranpar.splom(
  object,
  main = xpose.multiple.plot.title(object = object, plot.text =
    "Scatterplot matrix of random parameters", ...),
  varnames = NULL,
  onlyfirst = TRUE,
  smooth = TRUE,
  lmline = NULL,
  ...
)
cov.splom(
  object,
  main = xpose.multiple.plot.title(object = object, plot.text =
    "Scatterplot matrix of covariates", ...),
  varnames = NULL,
  onlyfirst = TRUE,
  smooth = TRUE,
  lmline = NULL,
  ...
)

parm.splom(
  object,
  main = xpose.multiple.plot.title(object = object, plot.text =
    "Scatterplot matrix of parameters", ...),
  varnames = NULL,
  onlyfirst = TRUE,
  smooth = TRUE,
  lmline = NULL,
  ...
)

ranpar.splom(
  object,
  main = xpose.multiple.plot.title(object = object, plot.text =
    "Scatterplot matrix of random parameters", ...),
  varnames = NULL,
  onlyfirst = TRUE,
  smooth = TRUE,
  lmline = NULL,
  ...
)

Arguments

`object`	An xpose.data object.
`main`	A string giving the plot title or `NULL` if none.
`varnames`	A vector of strings containing labels for the variables in the scatterplot matrix.
`onlyfirst`	Logical value indicating if only the first row per individual is included in the plot.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`lmline`	logical variable specifying whether a linear regression line should be superimposed over an `xyplot`. `NULL` ~ FALSE. (`y~x`)
`...`	Other arguments passed to `xpose.plot.histogram`.

Details

The parameters or covariates in the Xpose data object, as specified in object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar or object@Prefs@Xvardef$covariates, are plotted together as scatterplot matrices.

A wide array of extra options controlling scatterplot matrices are available. See xpose.plot.splom for details.

To control the appearance of the labels and names in the scatterplot matrix plots you can try varname.cex=0.5 and axis.text.cex=0.5 (this changes the tick labels and the variable names to be half as large as normal).

Value

Delivers a scatterplot matrix.

Functions

cov.splom(): A scatterplot matrix of covariates
parm.splom(): A scatterplot matrix of parameters
ranpar.splom(): A scatterplot matrix of random parameters

Author(s)

Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A scatterplot matrix of parameters, grouped by sex
parm.splom(xpdb, groups="SEX")

## A scatterplot matrix of ETAs, grouped by sex
ranpar.splom(xpdb, groups="SEX")

## Covariate scatterplots, with text customization
cov.splom(xpdb, varname.cex=0.4, axis.text.cex=0.4, smooth=NULL, cex=0.4)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A scatterplot matrix of parameters, grouped by sex
parm.splom(xpdb, groups="SEX")

## A scatterplot matrix of ETAs, grouped by sex
ranpar.splom(xpdb, groups="SEX")

## Covariate scatterplots, with text customization
cov.splom(xpdb, varname.cex=0.4, axis.text.cex=0.4, smooth=NULL, cex=0.4)

Create xpose.multiple.plot class.

Description

Creates a class for viewing and plotting xpose plots with multiple plots on the same page or multiple pages.

Usage

create.xpose.plot.classes()
create.xpose.plot.classes()

Author(s)

Niclas Jonsson and Andrew C. Hooker

This function creates the Xpose data classes ("xpose.data" and "xpose.prefs")

Description

This function defines and sets the Xpose data classes.

Usage

createXposeClasses(nm7 = F)
createXposeClasses(nm7 = F)

Arguments

nm7

FALSE if not using NONMEM 7.

Note

All the default settings are defined in this function.

Author(s)

Niclas Jonsson and Andrew C. Hooker

Histogram of conditional weighted residuals (CWRES), for Xpose 4

Description

This is a histogram of the distribution of conditional weighted residuals (CWRES) in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.histogram function.

Usage

cwres.dist.hist(object, ...)
cwres.dist.hist(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `xpose.plot.histogram`.

Details

Displays a histogram of the conditional weighted residuals (CWRES).

Value

Returns a histogram of conditional weighted residuals (CWRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
cwres.dist.hist(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
cwres.dist.hist(xpdb)

Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose 4

Description

This is a QQ plot of the distribution of conditional weighted residuals (CWRES) in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.qq function.

Usage

cwres.dist.qq(object, ...)
cwres.dist.qq(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.qq}`.

Details

Displays a QQ plot of the conditional weighted residuals (CWRES).

Value

Returns a QQ plot of conditional weighted residuals (CWRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


cwres.dist.qq(simpraz.xpdb)

cwres.dist.qq(simpraz.xpdb)

Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose 4

Description

This creates a stack of plots of conditional weighted residuals (CWRES) plotted against covariates, and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default and xpose.plot.histogram functions. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

cwres.vs.cov(
  object,
  ylb = "CWRES",
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)
cwres.vs.cov(
  object,
  ylb = "CWRES",
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}` or `link{xpose.plot.histogram}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots and histograms are available. See xpose.plot.default and xpose.plot.histogram for details.

Value

Returns a stack of xyplots and histograms of CWRES versus covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.cov(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.cov(xpdb)

Population conditional weighted residuals (CWRES) plotted against the independent variable (IDV) for Xpose 4

Description

This is a plot of population conditional weighted residuals (CWRES) vs the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

cwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)
cwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Conditional weighted residuals (CWRES) are plotted against the independent variable, as specified in object@Prefs@Xvardef$idv.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of CWRES vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
cwres.vs.idv(xpdb)

## A conditioning plot
cwres.vs.idv(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
cwres.vs.idv(xpdb)

## A conditioning plot
cwres.vs.idv(xpdb, by="HCTZ")

Box-and-whisker plot of conditional weighted residuals vs the independent variable for Xpose 4

Description

This creates a box and whisker plot of conditional weighted residuals (CWRES) vs the independent variable (IDV), and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

cwres.vs.idv.bw(object, ...)
cwres.vs.idv.bw(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.bw}`.

Details

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling bwplots are available. See xpose.plot.bw and xpose.panel.bw for details.

Value

Returns a stack of box-and-whisker plots of CWRES vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.idv.bw(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.idv.bw(xpdb)

Population conditional weighted residuals (CWRES) plotted against population predictions (PRED) for Xpose 4

Description

This is a plot of population conditional weighted residuals (cwres) vs population predictions (PRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

cwres.vs.pred(object, abline = c(0, 0), smooth = TRUE, ...)
cwres.vs.pred(object, abline = c(0, 0), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of CWRES vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.pred(xpdb)

## A conditioning plot
cwres.vs.pred(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.pred(xpdb)

## A conditioning plot
cwres.vs.pred(xpdb, by="HCTZ")

Box-and-whisker plot of conditional weighted residuals vs population predictions for Xpose 4

Description

This creates a box and whisker plot of conditional weighted residuals (CWRES) vs population predictions (PRED), and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

cwres.vs.pred.bw(object, ...)
cwres.vs.pred.bw(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.bw}`.

Details

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling bwplots are available. See xpose.plot.bw and xpose.panel.bw for details.

Value

Returns a box-and-whisker plot of CWRES vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.pred.bw(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

cwres.vs.pred.bw(xpdb)

Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the independent variable (IDV)

Description

A graphical comparison between the WRES and CWRES as plotted against the independent variable. Conditional weighted residuals (CWRES) require some extra steps to calculate. Either add CWRES to your NONMEM table files or compute them using the information proveded in compute.cwres. A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Usage

cwres.wres.vs.idv(
  object,
  ylb = "Residuals",
  abline = c(0, 0),
  smooth = TRUE,
  scales = list(),
  ...
)
cwres.wres.vs.idv(
  object,
  ylb = "Residuals",
  abline = c(0, 0),
  smooth = TRUE,
  scales = list(),
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`scales`	scales is passed to `xpose.plot.default`.
`...`	Other arguments passed to `xpose.plot.default`.

Value

A compound xyplot.

Author(s)

Niclas Jonsson & Andrew Hooker

Examples

cwres.wres.vs.idv(simpraz.xpdb)
cwres.wres.vs.idv(simpraz.xpdb)

Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the population predictions (PRED)

Description

Graphically compares WRES and CWRES as plotted against the population predictions.Conditional weighted residuals (CWRES) require some extra steps to calculate. Either add CWRES to your NONMEM table files or compute them using the information proveded in compute.cwres. A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Usage

cwres.wres.vs.pred(
  object,
  ylb = "Residuals",
  abline = c(0, 0),
  smooth = TRUE,
  scales = list(),
  ...
)
cwres.wres.vs.pred(
  object,
  ylb = "Residuals",
  abline = c(0, 0),
  smooth = TRUE,
  scales = list(),
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`scales`	scales is passed to `xpose.plot.default`
`...`	Other arguments passed to `xpose.plot.default`.

Value

A compound xyplot.

Author(s)

Niclas Jonsson & Andrew Hooker

Examples

cwres.wres.vs.pred(simpraz.xpdb)
cwres.wres.vs.pred(simpraz.xpdb)

Extract or assign data from an xpose.data object.

Description

Extracts or assigns the data from the Data or SData slots in an "xpose.data" object.

Usage

Data(object, inclZeroWRES = FALSE, onlyfirst = FALSE, subset = NULL)

Data(object, quiet = TRUE, keep.structure = F) <- value

SData(
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  subset = NULL,
  samp = NULL
)

SData(object) <- value
Data(object, inclZeroWRES = FALSE, onlyfirst = FALSE, subset = NULL)

Data(object, quiet = TRUE, keep.structure = F) <- value

SData(
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  subset = NULL,
  samp = NULL
)

SData(object) <- value

Arguments

`object`	An "xpose.data" object
`inclZeroWRES`	Logical value indicating whether rows with WRES==0 should be included in the extracted data.
`onlyfirst`	Logical value indicating whether only the first line per individual should be included in the extracted data.
`subset`	Expression with which the extracted data should be subset (see `xsubset`)
`quiet`	`TRUE or FALSE` if `FALSE` then some more information is printed out when adding data to an Xpose object.
`keep.structure`	`TRUE or FALSE` if`FALSE` then values are converted to continuous or categorical according to the rules set up by xpose using object@[email protected], object@[email protected] and the values in the "catab" file.
`value`	An R data.frame.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.

Details

When using Data to assign a data.frame to the Data slot in the "xpose.data" object a number of things happen:

Each column in the data.frame is checked and set to factor if the number of unique values are less than the value of Cat.levels (see xpose.prefs-class).

It is checked which of the predefined xpose data variables that exists in the data.frame. The variable definitions that does not exist are set to NULL.

The column identified by the dv xpose variable definition, is checked and set to factor if the number of unique values are less than or equal to the DV.Cat.levels (see xpose.prefs-class).

Finally, each column name in the data.frame is checked for a label (see xpose.prefs-class). If it is non-existent, the label is set to the column name.

When SData is used to assign a data.frame to the SData slot it is first checked that the number of rows in the SData data.frame is an even multiple of the number of rown in Data. Next, each column in the SData data.frame is assigned the same class as the corresponding column in the Data data.frame (it is required that the columns are the same in Data and SData). Finally, an extra column, "iter", is added to SData, which indicates the iteration number that each row belongs to. At the same time, the Nsim slot of the "xpose.data" object is set to the number of iterations (see nsim).

Value

Returns a data.frame from the Data or SData slots, excluding rows as indicated by the arguments.

Functions

Data(): Extract data
Data(object, quiet = TRUE, keep.structure = F) <- value: assign data
SData(): extract simulated data
SData(object) <- value: assign simulated data

Author(s)

Niclas Jonsson

Examples


xpdb <- simpraz.xpdb

## Extract data
my.dataframe <- Data(xpdb)

## Assign data
Data(xpdb) <- my.dataframe

## Extract simulated data
my.simulated.dataframe <- SData(xpdb)

## Assign simulated data
SData(xpdb) <- my.simulated.dataframe

xpdb <- simpraz.xpdb

## Extract data
my.dataframe <- Data(xpdb)

## Assign data
Data(xpdb) <- my.dataframe

## Extract simulated data
my.simulated.dataframe <- SData(xpdb)

## Assign simulated data
SData(xpdb) <- my.simulated.dataframe

Check through the source dataset to detect problems

Description

This function graphically "checks out" the dataset to identify errors or inconsistencies.

Usage

data.checkout(
  obj = NULL,
  datafile = ".ask.",
  hlin = -99,
  dotcol = "black",
  dotpch = 16,
  dotcex = 1,
  idlab = "ID",
  csv = NULL,
  main = "Default",
  ...
)
data.checkout(
  obj = NULL,
  datafile = ".ask.",
  hlin = -99,
  dotcol = "black",
  dotpch = 16,
  dotcex = 1,
  idlab = "ID",
  csv = NULL,
  main = "Default",
  ...
)

Arguments

`obj`	NULL or an xpose.data object.
`datafile`	A data file, suitable for import by `read.table`.
`hlin`	An integer, specifying the line number on which the column headers appear.
`dotcol`	Colour for the dots in the dotplot. If obj is an xpose data object then the default is to use the same value as defined for box-and-whisker plots.
`dotpch`	Plotting character for the dots in the dotplot. If obj is an xpose data object then the default is to use the same value as defined for box-and-whisker plots.
`dotcex`	Relative scaling for the dots in the dotplot. If obj is an xpose data object then the default is to use the same value as defined for box-and-whisker plots.
`idlab`	The ID column label in the dataset. Input as a text string.
`csv`	Is the data file in CSV format (comma separated values)? If the value is `NULL` then the user is asked at the command line. If supplied to the function the value can be `TRUE/FALSE`.
`main`	The title to the plot. "default" means that Xpose creates a title.
`...`	Other arguments passed to `link[lattice]{dotplot}`.

Details

This function creates a series of dotplots, one for each variable in the dataset, against individual ID. Outliers and clusters may easily be detected in this manner.

Value

A stack of dotplots.

Author(s)

Niclas Jonsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run, table and data files for run
## 5 in the current working directory 
xpdb5 <- xpose.data(5)

data.checkout(xpdb5, datafile = "mydata.dta")
data.checkout(datafile = "mydata.dta")

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run, table and data files for run
## 5 in the current working directory 
xpdb5 <- xpose.data(5)

data.checkout(xpdb5, datafile = "mydata.dta")
data.checkout(datafile = "mydata.dta")

## End(Not run)

Prints the contents of an Xpose data object

Description

These functions print a summary of the specified Xpose object to the R console.

Usage

db.names(object)
db.names(object)

Arguments

object

An xpose.data object.

Details

These functions return a detailed summary of the contents of a specified xpose.data object.

Value

A detailed summary of the contents of a specified xpose.data object.

Author(s)

Niclas Jonsson & Justin Wilkins

Examples

db.names(simpraz.xpdb)

db.names(simpraz.xpdb)

Change in individual objective function value vs. covariate value.

Description

Change in individual objective function value vs. covariate value.

Usage

dOFV.vs.cov(
  xpdb1,
  xpdb2,
  covariates = xvardef("covariates", xpdb1),
  ylb = expression(paste(Delta, OFV[i])),
  main = "Default",
  smooth = TRUE,
  abline = c(0, 0),
  ablcol = "grey",
  abllwd = 2,
  abllty = "dashed",
  max.plots.per.page = 1,
  ...
)
dOFV.vs.cov(
  xpdb1,
  xpdb2,
  covariates = xvardef("covariates", xpdb1),
  ylb = expression(paste(Delta, OFV[i])),
  main = "Default",
  smooth = TRUE,
  abline = c(0, 0),
  ablcol = "grey",
  abllwd = 2,
  abllty = "dashed",
  max.plots.per.page = 1,
  ...
)

Arguments

`xpdb1`	Xpose data object for first NONMEM run
`xpdb2`	Xpose data object for second NONMEM run
`covariates`	Covariates to plot against
`ylb`	Label for Y axis.
`main`	Title of plot.
`smooth`	Should we have a smooth?
`abline`	abline description.
`ablcol`	color of abline
`abllwd`	line width of abline
`abllty`	type of abline
`max.plots.per.page`	Plots per page.
`...`	additional arguments to function

Author(s)

Andrew C. Hooker

Examples


## Not run: 
## read in table files
xpdb8 <- xpose.data(8)
xpdb11 <- xpose.data(11)

## Make some plots
dOFV.vs.cov(xpdb8,xpdb11,"AGE")
dOFV.vs.cov(xpdb8,xpdb11,c("AGE","SECR"))

## End(Not run)

## Not run: 
## read in table files
xpdb8 <- xpose.data(8)
xpdb11 <- xpose.data(11)

## Make some plots
dOFV.vs.cov(xpdb8,xpdb11,"AGE")
dOFV.vs.cov(xpdb8,xpdb11,c("AGE","SECR"))

## End(Not run)

Change in Objective function value vs. removal of individuals.

Description

A plot showing the most and least influential individuals in determining a drop in OFV between two models.

Usage

dOFV.vs.id(
  xpdb1,
  xpdb2,
  sig.drop = -3.84,
  decrease.label.number = 3,
  increase.label.number = 3,
  id.lab.cex = 0.6,
  id.lab.pos = 2,
  type = "o",
  xlb = "Number of subjects removed",
  ylb = expression(paste(Delta, "OFV")),
  main = "Default",
  sig.line.col = "red",
  sig.line.lty = "dotted",
  tot.line.col = "grey",
  tot.line.lty = "dashed",
  key = list(columns = 1, lines = list(pch = c(super.sym$pch[1:2], NA, NA), type =
    list("o", "o", "l", "l"), col = c(super.sym$col[1:2], sig.line.col, tot.line.col),
    lty = c(super.sym$lty[1:2], sig.line.lty, tot.line.lty)), text =
    list(c(expression(paste(Delta, OFV[i] < 0)), expression(paste(Delta, OFV[i] > 0)),
    expression(paste("Significant  ", Delta, OFV)), expression(paste("Total  ", Delta,
    OFV)))), corner = c(0.95, 0.5), border = T),
  ...
)
dOFV.vs.id(
  xpdb1,
  xpdb2,
  sig.drop = -3.84,
  decrease.label.number = 3,
  increase.label.number = 3,
  id.lab.cex = 0.6,
  id.lab.pos = 2,
  type = "o",
  xlb = "Number of subjects removed",
  ylb = expression(paste(Delta, "OFV")),
  main = "Default",
  sig.line.col = "red",
  sig.line.lty = "dotted",
  tot.line.col = "grey",
  tot.line.lty = "dashed",
  key = list(columns = 1, lines = list(pch = c(super.sym$pch[1:2], NA, NA), type =
    list("o", "o", "l", "l"), col = c(super.sym$col[1:2], sig.line.col, tot.line.col),
    lty = c(super.sym$lty[1:2], sig.line.lty, tot.line.lty)), text =
    list(c(expression(paste(Delta, OFV[i] < 0)), expression(paste(Delta, OFV[i] > 0)),
    expression(paste("Significant  ", Delta, OFV)), expression(paste("Total  ", Delta,
    OFV)))), corner = c(0.95, 0.5), border = T),
  ...
)

Arguments

`xpdb1`	Xpose data object for first NONMEM run ("new" run)
`xpdb2`	Xpose data object for Second NONMEM run ("reference" run)
`sig.drop`	What is a significant drop of OFV?
`decrease.label.number`	How many points should bw labeled with ID values for those IDs with a drop in iOFV?
`increase.label.number`	How many points should bw labeled with ID values for those IDs with an increase in iOFV?
`id.lab.cex`	Size of ID labels.
`id.lab.pos`	ID label position.
`type`	Type of lines.
`xlb`	X-axis label.
`ylb`	Y-axis label.
`main`	Title of plot.
`sig.line.col`	Significant OFV drop line color.
`sig.line.lty`	Significant OFV drop line type.
`tot.line.col`	Total OFV drop line color.
`tot.line.lty`	Total OFV drop line type.
`key`	Legend for plot.
`...`	Additional arguments to function.

Author(s)

Andrew C. Hooker

Examples


## Not run: 
library(xpose4)

## first make sure that the iofv values are read into xpose
cur.dir <- getwd()
setwd(paste(cur.dir,"/LAG_TIME",sep=""))
xpdb1 <- xpose.data(1)
setwd(paste(cur.dir,"/TRANSIT_MODEL",sep=""))
xpdb2 <- xpose.data(1)
setwd(cur.dir)

## then make the plot
dOFV.vs.id(xpdb1,xpdb2)

## End(Not run)

## Not run: 
library(xpose4)

## first make sure that the iofv values are read into xpose
cur.dir <- getwd()
setwd(paste(cur.dir,"/LAG_TIME",sep=""))
xpdb1 <- xpose.data(1)
setwd(paste(cur.dir,"/TRANSIT_MODEL",sep=""))
xpdb2 <- xpose.data(1)
setwd(cur.dir)

## then make the plot
dOFV.vs.id(xpdb1,xpdb2)

## End(Not run)

Change in individual objective function value 1 vs. individual objective function value 2.

Description

Change in individual objective function value 1 vs. individual objective

Usage

dOFV1.vs.dOFV2(
  xpdb1,
  xpdb2,
  xpdb3,
  ylb = expression(paste(Delta, OFV1[i])),
  xlb = expression(paste(Delta, OFV2[i])),
  main = "Default",
  smooth = NULL,
  abline = c(0, 1),
  ablcol = "grey",
  abllwd = 2,
  abllty = "dashed",
  lmline = TRUE,
  ...
)
dOFV1.vs.dOFV2(
  xpdb1,
  xpdb2,
  xpdb3,
  ylb = expression(paste(Delta, OFV1[i])),
  xlb = expression(paste(Delta, OFV2[i])),
  main = "Default",
  smooth = NULL,
  abline = c(0, 1),
  ablcol = "grey",
  abllwd = 2,
  abllty = "dashed",
  lmline = TRUE,
  ...
)

Arguments

`xpdb1`	Xpose data object for first NONMEM run
`xpdb2`	Xpose data object for second NONMEM run
`xpdb3`	Xpose data object for third NONMEM run
`ylb`	Label for Y axis.
`xlb`	Label for X axis.
`main`	Title of plot.
`smooth`	Should we have a smooth?
`abline`	abline description.
`ablcol`	color of abline
`abllwd`	line width of abline
`abllty`	type of abline
`lmline`	Linear regression line?
`...`	Additional arguments to function.

Author(s)

Andrew C. Hooker

Examples


## Not run: 
## read in table files
xpdb8 <- xpose.data(8)
xpdb8 <- xpose.data(9)
xpdb11 <- xpose.data(11)

## Make the plot
dOFV.vs.cov(xpdb8,xpdb9,xpdb11)

## End(Not run)

## Not run: 
## read in table files
xpdb8 <- xpose.data(8)
xpdb8 <- xpose.data(9)
xpdb11 <- xpose.data(11)

## Make the plot
dOFV.vs.cov(xpdb8,xpdb9,xpdb11)

## End(Not run)

Observations (DV), individual predictions (IPRED) and population predictions (IPRED) plotted against the independent variable (IDV), for Xpose 4

Description

This is a compound plot consisting of plots of observations (DV), individual predictions (IPRED), and population predictions (PRED) against the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function.

Usage

dv.preds.vs.idv(
  object,
  ylb = "Observations/Predictions",
  layout = c(3, 1),
  smooth = TRUE,
  scales = list(),
  ...
)
dv.preds.vs.idv(
  object,
  ylb = "Observations/Predictions",
  layout = c(3, 1),
  smooth = TRUE,
  scales = list(),
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`layout`	A list controlling the number of columns and rows in a compound plot. The default is 2 columns and 1 row.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`scales`	A list to be used for the `scales` argument in `xyplot`.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a compound plot comprising plots of observations (DV), individual predictions (IPRED), and population predictions (PRED) against the independent variable (IDV).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

dv.preds.vs.idv(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

dv.preds.vs.idv(xpdb)

Observations (DV) plotted against the independent variable (IDV) for Xpose 4

Description

This is a plot of observations (DV) vs the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.idv(object, smooth = TRUE, ...)
dv.vs.idv(object, smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplot are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of DV vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

dv.vs.idv(xpdb)

## A conditioning plot
dv.vs.idv(xpdb, by="HCTZ")

## Logarithmic Y-axis
dv.vs.idv(xpdb, logy=TRUE)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

dv.vs.idv(xpdb)

## A conditioning plot
dv.vs.idv(xpdb, by="HCTZ")

## Logarithmic Y-axis
dv.vs.idv(xpdb, logy=TRUE)

Observations (DV) plotted against individual predictions (IPRED) for Xpose 4

Description

This is a plot of observations (DV) vs individual predictions (IPRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.ipred(object, abline = c(0, 1), smooth = TRUE, ...)
dv.vs.ipred(object, abline = c(0, 1), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplot are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of DV vs IPRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

dv.vs.ipred(xpdb)

## A conditioning plot
dv.vs.ipred(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

dv.vs.ipred(xpdb)

## A conditioning plot
dv.vs.ipred(xpdb, by="HCTZ")

Dependent variable vs individual predictions, conditioned on covariates, for Xpose 4

Description

This is a plot of dependent variable (DV) vs individual predictions (IPRED) conditioned by covariates, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.ipred.by.cov(
  object,
  covs = "Default",
  abline = c(0, 1),
  smooth = TRUE,
  main = "Default",
  ...
)
dv.vs.ipred.by.cov(
  object,
  covs = "Default",
  abline = c(0, 1),
  smooth = TRUE,
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`covs`	A vector of covariates to use in the plot. If "Default" the the covariates defined in `object@Prefs@Xvardef$Covariates` are used.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

A wide array of extra options controlling xyplot are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots of DV vs IPRED, conditioned on covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

dv.vs.ipred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

dv.vs.ipred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

Dependent variable vs individual predictions, conditioned on independent variable, for Xpose 4

Description

This is a plot of the dependent variable (DV) vs individual predictions (IPRED) conditioned by the independent variable, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.ipred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)
dv.vs.ipred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplot are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots of DV vs IPRED, conditioned on the independent variable.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

dv.vs.ipred.by.idv(simpraz.xpdb)

dv.vs.ipred.by.idv(simpraz.xpdb)

Observations (DV) plotted against population predictions (PRED) for Xpose 4

Description

This is a plot of observations (DV) vs population predictions (PRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.pred(object, abline = c(0, 1), smooth = TRUE, ...)
dv.vs.pred(object, abline = c(0, 1), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of DV vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
dv.vs.pred(xpdb)

## A conditioning plot
dv.vs.pred(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A vanilla plot
dv.vs.pred(xpdb)

## A conditioning plot
dv.vs.pred(xpdb, by="HCTZ")

Dependent variable vs population predictions, conditioned on covariates, for Xpose 4

Description

This is a plot of the dependent variable (DV) vs population predictions (PRED) conditioned by covariates, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.pred.by.cov(
  object,
  covs = "Default",
  abline = c(0, 1),
  smooth = TRUE,
  main = "Default",
  ...
)
dv.vs.pred.by.cov(
  object,
  covs = "Default",
  abline = c(0, 1),
  smooth = TRUE,
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`covs`	A vector of covariates to use in the plot. If "Default" the the covariates defined in `object@Prefs@Xvardef$Covariates` are used.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots of DV vs PRED, conditioned on covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

dv.vs.pred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

dv.vs.pred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

Dependent variable vs population predictions, conditioned on independent variable, for Xpose 4

Description

This is a plot of the dependent variable (DV) vs population predictions (PRED) conditioned by the independent variable, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

dv.vs.pred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)
dv.vs.pred.by.idv(object, abline = c(0, 1), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots of DV vs PRED, conditioned on the independent variable.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

dv.vs.pred.by.idv(simpraz.xpdb)

dv.vs.pred.by.idv(simpraz.xpdb)

Observations (DV) are plotted against individual predictions (IPRED) and population predictions (PRED), for Xpose 4

Description

This is a compound plot consisting of plots of observations (DV) against individual predictions (IPRED) and population predictions (PRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function.

Usage

dv.vs.pred.ipred(
  object,
  xlb = "Predictions",
  layout = c(2, 1),
  abline = c(0, 1),
  lmline = TRUE,
  smooth = NULL,
  scales = list(),
  ...
)
dv.vs.pred.ipred(
  object,
  xlb = "Predictions",
  layout = c(2, 1),
  abline = c(0, 1),
  lmline = TRUE,
  smooth = NULL,
  scales = list(),
  ...
)

Arguments

`object`	An xpose.data object.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`layout`	A list giving the layout of the graphs on the plot, in columns and rows.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`lmline`	logical variable specifying whether a linear regression line should be superimposed over an `xyplot`. `NULL` ~ FALSE. (`y~x`)
`smooth`	`NULL` or `TRUE` value indicating whether an x-y smooth should be superimposed.
`scales`	A list to be used for the `scales` argument in `xyplot`.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Plots of DV vs PRED and IPRED are presented side by side for comparison.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a compound plot comprising plots of observations (DV) against individual predictions (IPRED) and population predictions (PRED).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

dv.vs.pred.ipred(simpraz.xpdb)

dv.vs.pred.ipred(simpraz.xpdb)

Exports Xpose graphics settings to a file.

Description

This function exports graphics settings for a specified Xpose data object to a file.

Usage

export.graph.par(object)

xpose.write(object, file = "xpose.ini")
export.graph.par(object)

xpose.write(object, file = "xpose.ini")

Arguments

`object`	An `xpose.data` object.
`file`	The file to contain exported Xpose settings.

Details

This function exports the graphics settings (contents of object@[email protected]) for a given xpose.data object to a file, typically 'xpose.ini'. It is a wrapper for xpose.write. Note that the file format is not the same as is used in import.variable.definitions and export.variable.definitions.

Value

Null.

Functions

xpose.write(): export graphics settings for a specified Xpose data object to a file.

Author(s)

Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## For a filename prompt
export.graph.par(xpdb5)

## Command-line driven
xpose.write(xpdb5, "c:/XposeSettings/mytheme.ini")

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## For a filename prompt
export.graph.par(xpdb5)

## Command-line driven
xpose.write(xpdb5, "c:/XposeSettings/mytheme.ini")

## End(Not run)

Exports Xpose variable definitions to a file from an Xpose data object.

Description

This function exports variable definitions for a specified Xpose data object to a file.

Usage

export.variable.definitions(object, file = "")
export.variable.definitions(object, file = "")

Arguments

`object`	An `xpose.data` object.
`file`	A file name as a string.

Details

This function exports variable definitions (contents of object@Prefs@Xvardef) for a given xpose.data object to a file, typically 'xpose.vardefs.ini'. Note that file format is not the same as used for graphics settings. It is a wrapper for the R function dput.

Value

Null.

Author(s)

Niclas Jonsson & Justin Wilkins

Examples

od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

export.variable.definitions(simpraz.xpdb,file="xpose.vardefs.ini")
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

file.remove(new.files) # remove this file
setwd(od)  # restore working directory


od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

export.variable.definitions(simpraz.xpdb,file="xpose.vardefs.ini")
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

file.remove(new.files) # remove this file
setwd(od)  # restore working directory

GAM functions for Xpose 4

Description

These are functions for summarizing and plotting the results of the generalized additive model within Xpose

Usage

xp.akaike.plot(
  gamobj = NULL,
  title = "Default",
  xlb = "Akaike value",
  ylb = "Models",
  ...
)

xp.cook(gam.object)

xp.ind.inf.fit(
  gamobj = NULL,
  plot.ids = TRUE,
  idscex = 0.7,
  ptscex = 0.7,
  title = "Default",
  recur = FALSE,
  xlb = NULL,
  ylb = NULL,
  ...
)

xp.ind.inf.terms(
  gamobj = NULL,
  xlb = NULL,
  ylb = NULL,
  plot.ids = TRUE,
  idscex = 0.7,
  ptscex = 0.7,
  prompt = TRUE,
  ...
)

xp.ind.stud.res(
  gamobj = NULL,
  title = "Default",
  recur = FALSE,
  xlb = NULL,
  ylb = NULL
)

xp.plot(
  gamobj = NULL,
  plot.ids = TRUE,
  idscex = 0.7,
  ptscex = 0.7,
  prompt = TRUE,
  ...
)

xp.summary(gamobj = NULL)
xp.akaike.plot(
  gamobj = NULL,
  title = "Default",
  xlb = "Akaike value",
  ylb = "Models",
  ...
)

xp.cook(gam.object)

xp.ind.inf.fit(
  gamobj = NULL,
  plot.ids = TRUE,
  idscex = 0.7,
  ptscex = 0.7,
  title = "Default",
  recur = FALSE,
  xlb = NULL,
  ylb = NULL,
  ...
)

xp.ind.inf.terms(
  gamobj = NULL,
  xlb = NULL,
  ylb = NULL,
  plot.ids = TRUE,
  idscex = 0.7,
  ptscex = 0.7,
  prompt = TRUE,
  ...
)

xp.ind.stud.res(
  gamobj = NULL,
  title = "Default",
  recur = FALSE,
  xlb = NULL,
  ylb = NULL
)

xp.plot(
  gamobj = NULL,
  plot.ids = TRUE,
  idscex = 0.7,
  ptscex = 0.7,
  prompt = TRUE,
  ...
)

xp.summary(gamobj = NULL)

Arguments

`gamobj`	A GAM object to use in the plot. IF null then the user is asked to choose from a list of GAM objects in memory.
`title`	A text string indicating plot title. If `NULL`, left blank.
`xlb`	A text string indicating x-axis legend. If `NULL`, left blank.
`ylb`	A text string indicating y-axis legend. If `NULL`, left blank.
`...`	Other arguments passed to the GAM functions.
`gam.object`	A GAM object (see `gam`.
`plot.ids`	Logical, specifies whether or not ID numbers should be displayed.
`idscex`	ID label size.
`ptscex`	Point size.
`recur`	If dispersion should be used in the GAM object.
`prompt`	Specifies whether or not the user should be prompted to press RETURN between plot pages. Default is TRUE.
`object`	An xpose.data object.

Value

Plots or summaries.

Functions

xp.akaike.plot(): An Akaike plot of the results.
xp.cook(): Individual parameters to GAM fit.
xp.ind.inf.fit(): Individual influence on GAM fit.
xp.ind.inf.terms(): Individual influence on GAM terms.
xp.ind.stud.res(): Studentized residuals.
xp.plot(): GAM residuals of base model vs. covariates.
xp.summary(): Summarize GAM.

Author(s)

Niclas Jonsson & Andrew Hooker

Structured goodness of fit diagnostics.

Description

This is a template function for creating structured goodness of fit diagnostics using the functions in the Xpose specific library.

Usage

gof(
  runno = NULL,
  save = FALSE,
  onefile = FALSE,
  saveType = "pdf",
  pageWidth = 7.6,
  pageHeight = 4.9,
  structural = TRUE,
  residual = TRUE,
  covariate = FALSE,
  iiv = FALSE,
  iov = FALSE,
  all = FALSE,
  myTrace = xpPage
)
gof(
  runno = NULL,
  save = FALSE,
  onefile = FALSE,
  saveType = "pdf",
  pageWidth = 7.6,
  pageHeight = 4.9,
  structural = TRUE,
  residual = TRUE,
  covariate = FALSE,
  iiv = FALSE,
  iov = FALSE,
  all = FALSE,
  myTrace = xpPage
)

Arguments

`runno`	The run number fo Xpose to identify the appropriate files to read. In addition `runno` is used to construct the file name to save plots in. `runno` can also be `NULL` for cases in which the function is used for non-Xpose based code.
`save`	Logical. `TRUE` if the plot(s) is to be saved in a file. `FALSE` if the plot(s) is to be displayed on screen. The plot(s) will be saved in a file named with the function name followed by the word 'run', the run number, an order number followed by a file name extension appropriate for the selected `saveType`. For example 'gofrun1-01.pdf' for the first plot file created by a script called `gof` based on output from run 1 and `saveType='pdf'`.
`onefile`	Logical. `TRUE` if plots are to be save in a single file and `FALSE` if each plot should be saved as a separate file. In the latter case, each file will be have an incremented order number (01-99).
`saveType`	The type of graphics file to produce if `save=TRUE`. Allowed values are 'pdf' (default), 'wmf' (only Windows) and 'png'.
`pageWidth`	The width of the graphics device in inches.
`pageHeight`	The height of the graphics device in inches.
`structural`	Logical. `TRUE` if the code in the structural model section (see below) should be executed and `FALSE` if not.
`residual`	Logical. `TRUE` if the code in the residual model section (see below) should be executed and `FALSE` if not.
`covariate`	Logical. `TRUE` if the code in the covariate model section (see below) should be executed and `FALSE` if not.
`iiv`	Logical. `TRUE` if the code in the IIV model section (see below) should be executed and `FALSE` if not.
`iov`	Logical. `TRUE` if the code in the IOV model section (see below) should be executed and `FALSE` if not.
`all`	Logical. `TRUE` if the code in all sections (see below) should be executed.
`myTrace`	`NULL` or the name of a function. The value of `myTrace` can used with the lattice `page=` argument to annotate plots for traceability.

Details

The gof function is provided as a template to facilitate the (structured) use of the functions in the Xpose specific library. Xpose specific is extensively described in the 'Xpose Bestiary'.

The function can be renamed so that multiple scripts can be used in parallel.

The function is set up to make it easy to display plots on screen as well as to save them in files. In the latter case, plots are save in a sub-directory called 'Plots'.

The arguments structural, residual, covariate, iiv, iov and all are just "switches" to different parts of the code (if-blocks). These blocks can be removed or the default values of the arguments changed to better suit the needs of the user.

It is also possible to add tracing information to the produced plots. This is done via the myTrace argument. A non-NULL value should be a function that returns a panel.text object. The default is the xpPage function that will put a string concatenated from the device name, function name, working directory and date, in small, faint grey, font at the bottom of each graph page. Note that the user need to add page=myTrace as an argument to the Xpose functions for this to have an effect.

The function calls a support function called gofSetup, which is responsible for setting up the graphics device and determining the file names for saved graphs.

Value

Does not return anything unless the user specify a return value.

Author(s)

E. Niclas Jonsson, Mats Karlsson and Andrew Hooker

Examples


## Not run: 
## This is an example of how the function may be setup by a user.

library(xpose4)
mygof <- gof
fix(mygof)

myggof <- function (runno = NULL, save = FALSE, onefile = FALSE, saveType = "pdf", 
         pageWidth = 7.6, pageHeight = 4.9, structural = TRUE, residual = TRUE, 
         covariate = FALSE, iiv = FALSE, iov = FALSE, all = FALSE, myTrace=xpPage) {

         gofSetup(runno, save, onefile, saveType, pageWidth, pageHeight)
         xpdb <- xpose.data(runno)

         if (structural || all) {
          xplot <- dv.vs.pred.ipred(xpdb, page = myPage)
          print(xplot)
         }
         if (residual || all) {
          xplot <- absval.wres.vs.pred(xpdb, page = myPage)
          print(xplot)
         }
         if (covariate || all) {
         }
         if (iiv || all) {
         }
         if (iov || all) {
         }
         if (save) dev.off()
    invisible()
  }

## The function can then be execute, e.g.:
mygof(1)


## End(Not run)

## Not run: 
## This is an example of how the function may be setup by a user.

library(xpose4)
mygof <- gof
fix(mygof)

myggof <- function (runno = NULL, save = FALSE, onefile = FALSE, saveType = "pdf", 
         pageWidth = 7.6, pageHeight = 4.9, structural = TRUE, residual = TRUE, 
         covariate = FALSE, iiv = FALSE, iov = FALSE, all = FALSE, myTrace=xpPage) {

         gofSetup(runno, save, onefile, saveType, pageWidth, pageHeight)
         xpdb <- xpose.data(runno)

         if (structural || all) {
          xplot <- dv.vs.pred.ipred(xpdb, page = myPage)
          print(xplot)
         }
         if (residual || all) {
          xplot <- absval.wres.vs.pred(xpdb, page = myPage)
          print(xplot)
         }
         if (covariate || all) {
         }
         if (iiv || all) {
         }
         if (iov || all) {
         }
         if (save) dev.off()
    invisible()
  }

## The function can then be execute, e.g.:
mygof(1)


## End(Not run)

Imports Xpose graphics settings from a file to an Xpose data object.

Description

This function imports graphics settings for a specified Xpose data object from a file.

Usage

import.graph.par(object, classic = FALSE)
import.graph.par(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

This function imports graphics settings (contents of object@[email protected]) for a given xpose.data object from a file, typically 'xpose.ini'. It is a wrapper for xpose.read. It returns an xpose.data object. Note that the file format is not the same as is used in import.variable.definitions and export.variable.definitions.

Value

An xpose.data object (classic = FALSE) or null (classic = TRUE).

Author(s)

Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Import graphics preferences you saved earlier using export.graph.par
xpdb5 <- import.graph.par(xpdb5)

## Command-line driven
xpdb5 <- xpose.read(xpdb5, "c:/XposeSettings/mytheme.ini")

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Import graphics preferences you saved earlier using export.graph.par
xpdb5 <- import.graph.par(xpdb5)

## Command-line driven
xpdb5 <- xpose.read(xpdb5, "c:/XposeSettings/mytheme.ini")

## End(Not run)

Imports Xpose variable definitions from a file to an Xpose data object.

Description

This function imports variable definitions for a specified Xpose data object from a file.

Usage

import.variable.definitions(object, classic = FALSE)
import.variable.definitions(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

This function imports variable definitions (contents of object@Prefs@Xvardef) for a given xpose.data object from a file, typically 'xpose.vardefs.ini'. It returns an xpose.data object. Note that file format is not the same as used for graphics settings. It is a wrapper for the R function dget.

Value

An xpose.data object (classic == FALSE) or null (classic == TRUE).

Author(s)

Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpdb5 <- import.variable.definitions(xpdb5)

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpdb5 <- import.variable.definitions(xpdb5)

## End(Not run)

Observations (DV), individual predictions (IPRED) and population predictions (PRED) are plotted against the independent variable for every individual in the dataset, for Xpose 4

Description

This is a compound plot consisting of plots of observations (DV), individual predictions (IPRED) and population predictions (PRED) against the independent variable for every individual in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function.

Usage

ind.plots(
  object,
  y.vals = c(xvardef("dv", new.obj), xvardef("ipred", new.obj), xvardef("pred", new.obj)),
  x.vals = xvardef("idv", new.obj),
  id.vals = xvardef("id", new.obj),
  key.text = y.vals,
  main = "Default",
  key = "Default",
  xlb = xlabel(xvardef("idv", object), object),
  ylb = NULL,
  layout = c(4, 4),
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  type = "o",
  grid = FALSE,
  col = c(1, 2, 4),
  lty = c(0, 1, 3),
  lwd = c(1, 1, 1),
  pch = c(21, 32, 32),
  cex = c(0.7, 0.7, 0.7),
  fill = c("lightgrey", 0, 0),
  prompt = FALSE,
  mirror = NULL,
  main.cex = 0.9,
  max.plots.per.page = 1,
  pch.ip.sp = c(21, 19, 18),
  cex.ip.sp = c(0.7, 0.4, 0.4),
  y.vals.subset = NULL,
  ...
)
ind.plots(
  object,
  y.vals = c(xvardef("dv", new.obj), xvardef("ipred", new.obj), xvardef("pred", new.obj)),
  x.vals = xvardef("idv", new.obj),
  id.vals = xvardef("id", new.obj),
  key.text = y.vals,
  main = "Default",
  key = "Default",
  xlb = xlabel(xvardef("idv", object), object),
  ylb = NULL,
  layout = c(4, 4),
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  type = "o",
  grid = FALSE,
  col = c(1, 2, 4),
  lty = c(0, 1, 3),
  lwd = c(1, 1, 1),
  pch = c(21, 32, 32),
  cex = c(0.7, 0.7, 0.7),
  fill = c("lightgrey", 0, 0),
  prompt = FALSE,
  mirror = NULL,
  main.cex = 0.9,
  max.plots.per.page = 1,
  pch.ip.sp = c(21, 19, 18),
  cex.ip.sp = c(0.7, 0.4, 0.4),
  y.vals.subset = NULL,
  ...
)

Arguments

`object`	An xpose.data object.
`y.vals`	The Y values to use.
`x.vals`	The X values to use.
`id.vals`	The ID values to use.
`key.text`	The text in the legend to use.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`key`	Create a legend.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`layout`	A list giving the layout of the graphs on the plot, in columns and rows. The default is 4x4.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot. The default is TRUE.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`type`	1-character string giving the type of plot desired. The default is "o", for over-plotted points and lines. See `xpose.plot.default`.
`grid`	Should the plots have a grid in each plot?
`col`	A list of three elements, giving plotting characters for observations, individual predictions, and population predictions, in that order. The default is black for DV, red for individual predictions, and blue for population predictions.
`lty`	A list of three elements, giving line types for observations, individual predictions, and population predictions, in that order.
`lwd`	A list of three elements, giving line widths for observations, individual predictions, and population predictions, in that order.
`pch`	A list of three elements, giving plotting characters for observations, individual predictions, and population predictions, in that order.
`cex`	A list of three elements, giving relative point size for observations, individual predictions, and population predictions, in that order. The default is c(0.7,0.7,0.7).
`fill`	Fill the circles in the points?
`prompt`	Specifies whether or not the user should be prompted to press RETURN between plot pages. Default is TRUE.
`mirror`	Mirror plots are not yet implemented in this function and this argument must contain a value of `NULL`
`main.cex`	The size of the title.
`max.plots.per.page`	Maximum number of plots per page.
`pch.ip.sp`	If there is a panel with just one observation then this specifies the type of points for the DV, IPRED and PRED respectively.
`cex.ip.sp`	If there is a panel with just one observation then this specifies the size of the points for the DV, IPRED and PRED respectively.
`y.vals.subset`	Used to subset on the DV, IPRED and PRED variables separately. Either `NULL` or a vector of three strings, corresponding to the subset of DV, IPRED and PRED respectively. See examples below.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Matrices of individual plots are presented for comparison and closer inspection.

Value

Returns a stack of plots observations (DV) against individual predictions (IPRED) and population predictions (PRED).

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## Monochrome, suitable for manuscript or report
ind.plots(xpdb, 
  subset="ID>40 & ID<57", 
  col=c(1,1,1), 
  lty=c(0,2,3), 
  strip=function(..., bg) 
    strip.default(..., bg="grey"))
    
## Not run:     
## IF we simulate in NONMEM using a dense grid of time points
## and all non-observed DV items are equal to zero.
ind.plots(xpdb,inclZeroWRES=TRUE,y.vals.subset=c("DV!=0","NULL","NULL"))


# to plot individual plots of multiple variables
ind.plots(xpdb,subset="FLAG==1")
ind.plots(xpdb,subset="FLAG==2")

## End(Not run)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## Monochrome, suitable for manuscript or report
ind.plots(xpdb, 
  subset="ID>40 & ID<57", 
  col=c(1,1,1), 
  lty=c(0,2,3), 
  strip=function(..., bg) 
    strip.default(..., bg="grey"))
    
## Not run:     
## IF we simulate in NONMEM using a dense grid of time points
## and all non-observed DV items are equal to zero.
ind.plots(xpdb,inclZeroWRES=TRUE,y.vals.subset=c("DV!=0","NULL","NULL"))


# to plot individual plots of multiple variables
ind.plots(xpdb,subset="FLAG==1")
ind.plots(xpdb,subset="FLAG==2")

## End(Not run)

Histograms of weighted residuals for each individual in an Xpose data object, for Xpose 4

Description

This is a compound plot consisting of histograms of the distribution of weighted residuals (any weighted residual available from NONMEM) for every individual in the dataset. It is a wrapper encapsulating arguments to the xpose.plot.histogram function.

Usage

ind.plots.cwres.hist(object, wres = "cwres", ...)

ind.plots.wres.hist(
  object,
  main = "Default",
  wres = "wres",
  ylb = NULL,
  layout = c(4, 4),
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  scales = list(cex = 0.7, tck = 0.5),
  aspect = "fill",
  force.by.factor = TRUE,
  ids = F,
  as.table = TRUE,
  hicol = object@[email protected]$hicol,
  hilty = object@[email protected]$hilty,
  hilwd = object@[email protected]$hilwd,
  hidcol = object@[email protected]$hidcol,
  hidlty = object@[email protected]$hidlty,
  hidlwd = object@[email protected]$hidlwd,
  hiborder = object@[email protected]$hiborder,
  prompt = FALSE,
  mirror = NULL,
  main.cex = 0.9,
  max.plots.per.page = 1,
  ...
)
ind.plots.cwres.hist(object, wres = "cwres", ...)

ind.plots.wres.hist(
  object,
  main = "Default",
  wres = "wres",
  ylb = NULL,
  layout = c(4, 4),
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  scales = list(cex = 0.7, tck = 0.5),
  aspect = "fill",
  force.by.factor = TRUE,
  ids = F,
  as.table = TRUE,
  hicol = object@Prefs@Graph.prefs$hicol,
  hilty = object@Prefs@Graph.prefs$hilty,
  hilwd = object@Prefs@Graph.prefs$hilwd,
  hidcol = object@Prefs@Graph.prefs$hidcol,
  hidlty = object@Prefs@Graph.prefs$hidlty,
  hidlwd = object@Prefs@Graph.prefs$hidlwd,
  hiborder = object@Prefs@Graph.prefs$hiborder,
  prompt = FALSE,
  mirror = NULL,
  main.cex = 0.9,
  max.plots.per.page = 1,
  ...
)

Arguments

`object`	An xpose.data object.
`wres`	Which weighted residual should we plot? Defaults to the WRES.
`...`	Other arguments passed to `xpose.plot.histogram`.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`layout`	A list giving the layout of the graphs on the plot, in columns and rows. The default is 4x4.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot. The default is FALSE.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`scales`	see `xpose.plot.histogram`
`aspect`	see `xpose.plot.histogram`
`force.by.factor`	see `xpose.plot.histogram`
`ids`	see `xpose.plot.histogram`
`as.table`	see `xpose.plot.histogram`
`hicol`	the fill colour of the histogram - an integer or string. The default is blue (see `histogram`).
`hilty`	the border line type of the histogram - an integer. The default is 1 (see `histogram`).
`hilwd`	the border line width of the histogram - an integer. The default is 1 (see `histogram`).
`hidcol`	the fill colour of the density line - an integer or string. The default is black (see `histogram`).
`hidlty`	the border line type of the density line - an integer. The default is 1 (see `histogram`).
`hidlwd`	the border line width of the density line - an integer. The default is 1 (see `histogram`).
`hiborder`	the border colour of the histogram - an integer or string. The default is black (see `histogram`).
`prompt`	Specifies whether or not the user should be prompted to press RETURN between plot pages. Default is FALSE.
`mirror`	Mirror plots are not yet implemented in this function and this argument must contain a value of `NULL`
`main.cex`	The size of the title.
`max.plots.per.page`	Maximum number of plots per page

Details

Matrices of histograms of weighted residuals in each included individual are displayed. ind.plots.cwres.hist is just a wrapper for ind.plots.wres.hist(object,wres="cwres").

Value

Returns a compound plot comprising histograms of weighted residual conditioned on individual.

Functions

ind.plots.cwres.hist(): Histograms of conditional weighted residuals for each individual

Author(s)

E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A plot of the first 16 individuals
ind.plots.cwres.hist(xpdb, subset="ID<18")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## A plot of the first 16 individuals
ind.plots.cwres.hist(xpdb, subset="ID<18")

Quantile-quantile plots of weighted residuals for each individual in an Xpose data object, for Xpose 4

Description

This is a compound plot consisting of QQ plots of the distribution of weighted residuals (any weighted residual produced by NONMEM) for every individual in the dataset. The function is a wrapper encapsulating arguments to the xpose.plot.qq function.

Usage

ind.plots.cwres.qq(object, wres = "cwres", ...)

ind.plots.wres.qq(
  object,
  main = "Default",
  wres = "wres",
  layout = c(4, 4),
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  scales = list(cex = 0.7, tck = 0.5),
  aspect = "fill",
  force.by.factor = TRUE,
  ids = F,
  as.table = TRUE,
  type = "o",
  pch = object@[email protected]$pch,
  col = object@[email protected]$col,
  cex = object@[email protected]$cex,
  abllty = object@[email protected]$abllty,
  abllwd = object@[email protected]$abllwd,
  ablcol = object@[email protected]$ablcol,
  prompt = FALSE,
  main.cex = 0.9,
  mirror = NULL,
  max.plots.per.page = 1,
  ...
)
ind.plots.cwres.qq(object, wres = "cwres", ...)

ind.plots.wres.qq(
  object,
  main = "Default",
  wres = "wres",
  layout = c(4, 4),
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  scales = list(cex = 0.7, tck = 0.5),
  aspect = "fill",
  force.by.factor = TRUE,
  ids = F,
  as.table = TRUE,
  type = "o",
  pch = object@Prefs@Graph.prefs$pch,
  col = object@Prefs@Graph.prefs$col,
  cex = object@Prefs@Graph.prefs$cex,
  abllty = object@Prefs@Graph.prefs$abllty,
  abllwd = object@Prefs@Graph.prefs$abllwd,
  ablcol = object@Prefs@Graph.prefs$ablcol,
  prompt = FALSE,
  main.cex = 0.9,
  mirror = NULL,
  max.plots.per.page = 1,
  ...
)

Arguments

`object`	An xpose.data object.
`wres`	Which weighted residual should we plot? Defaults to the WRES.
`...`	Other arguments passed to `link{xpose.plot.qq}`.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`layout`	A list giving the layout of the graphs on the plot, in columns and rows. The default is 4x4.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot. The default is FALSE.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`scales`	See `xpose.plot.qq`.
`aspect`	See `xpose.plot.qq`.
`force.by.factor`	See `xpose.plot.qq`.
`ids`	See `xpose.plot.qq`.
`as.table`	See `xpose.plot.qq`.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`pch`	The plotting character, or symbol, to use. Specified as an integer. See R help on `points`. The default is an open circle.
`col`	The color for lines and points. Specified as an integer or a text string. A full list is obtained by the R command `colours()`. The default is blue (col=4).
`cex`	The amount by which plotting text and symbols should be scaled relative to the default. 'NULL' and 'NA' are equivalent to '1.0'.
`abllty`	Line type of the line of identity.
`abllwd`	Line width of the line of identity.
`ablcol`	Line colour of the line of identity.
`prompt`	Specifies whether or not the user should be prompted to press RETURN between plot pages. Default is FALSE.
`main.cex`	The size of the title.
`mirror`	Mirror plots are not yet implemented in this function and this argument must contain a value of `NULL`
`max.plots.per.page`	Maximum number of plots per page

Details

Matrices of Q-Q plots of weighted residuals in each included individual are displayed.

A wide array of extra options controlling Q-Q plots are available. See xpose.plot.qq for details.

Value

Returns a compound plot comprising QQ plots of weighted residuals conditioned on individual.

Functions

ind.plots.cwres.qq(): Q-Q plots of conditional weighted residuals for each individual

Author(s)

E. Niclas Jonsson, Mats Karlsson, Justin Wilkins & Andrew Hooker

Examples


ind.plots.cwres.qq(simpraz.xpdb,subset="ID<18")

ind.plots.cwres.qq(simpraz.xpdb,subset="ID<18")

Individual predictions (IPRED) plotted against the independent variable (IDV) for Xpose 4

Description

This is a plot of Individual predictions (IPRED) vs the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

ipred.vs.idv(object, smooth = TRUE, ...)
ipred.vs.idv(object, smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of IPRED vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

ipred.vs.idv(xpdb)

## A conditioning plot
ipred.vs.idv(xpdb, by="HCTZ")

## Logarithmic Y-axis
ipred.vs.idv(xpdb, logy=TRUE)

## Custom colours and symbols, IDs
ipred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

ipred.vs.idv(xpdb)

## A conditioning plot
ipred.vs.idv(xpdb, by="HCTZ")

## Logarithmic Y-axis
ipred.vs.idv(xpdb, logy=TRUE)

## Custom colours and symbols, IDs
ipred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

Histogram of individual weighted residuals (IWRES), for Xpose 4

Description

This is a histogram of the distribution of individual weighted residuals (IWRES) in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.histogram function.

Usage

iwres.dist.hist(object, ...)
iwres.dist.hist(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `xpose.plot.histogram`.

Details

Displays a histogram of the individual weighted residuals (IWRES).

Value

Returns a histogram of individual weighted residuals (IWRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

iwres.dist.hist(simpraz.xpdb)

iwres.dist.hist(simpraz.xpdb)

Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4

Description

This is a QQ plot of the distribution of individual weighted residuals (IWRES) in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.qq function.

Usage

iwres.dist.qq(object, ...)
iwres.dist.qq(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.qq}`.

Details

Displays a QQ plot of the individual weighted residuals (IWRES).

Value

Returns a QQ plot of individual weighted residuals (IWRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

iwres.dist.qq(simpraz.xpdb)

iwres.dist.qq(simpraz.xpdb)

Individual weighted residuals (IWRES) plotted against the independent variable (IDV) for Xpose 4

Description

This is a plot of individual weighted residuals (IWRES) vs the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

iwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)
iwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`. Here, the default is c(0,0), specifying a horizontal line at y=0.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of IWRES vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

iwres.vs.idv(xpdb)

## A conditioning plot
iwres.vs.idv(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

iwres.vs.idv(xpdb)

## A conditioning plot
iwres.vs.idv(xpdb, by="HCTZ")

Kaplan-Meier plots of (repeated) time-to-event data

Description

Kaplan-Meier plots of (repeated) time-to-event data. Includes VPCs.

Usage

kaplan.plot(
  x = "TIME",
  y = "DV",
  id = "ID",
  data = NULL,
  evid = "EVID",
  by = NULL,
  xlab = "Time",
  ylab = "Default",
  object = NULL,
  events.to.plot = "All",
  sim.data = NULL,
  sim.zip.file = NULL,
  VPC = FALSE,
  nsim.lab = "simNumber",
  sim.evct.lab = "counter",
  probs = c(0.025, 0.975),
  add.baseline = T,
  add.last.area = T,
  subset = NULL,
  main = "Default",
  main.sub = "Default",
  main.sub.cex = 0.8,
  nbins = NULL,
  real.type = "l",
  real.lty = 1,
  real.lwd = 1,
  real.col = "blue",
  real.se = if (!is.null(sim.data)) F else T,
  real.se.type = "l",
  real.se.lty = 2,
  real.se.lwd = 0.5,
  real.se.col = "red",
  cens.type = "l",
  cens.lty = 1,
  cens.col = "black",
  cens.lwd = 1,
  cens.rll = 0.02,
  inclZeroWRES = TRUE,
  onlyfirst = FALSE,
  samp = NULL,
  poly.alpha = 1,
  poly.fill = "lightgreen",
  poly.line.col = "darkgreen",
  poly.lty = 2,
  censor.lines = TRUE,
  ylim = c(-5, 105),
  cov = NULL,
  cov.fun = "mean",
  ...
)
kaplan.plot(
  x = "TIME",
  y = "DV",
  id = "ID",
  data = NULL,
  evid = "EVID",
  by = NULL,
  xlab = "Time",
  ylab = "Default",
  object = NULL,
  events.to.plot = "All",
  sim.data = NULL,
  sim.zip.file = NULL,
  VPC = FALSE,
  nsim.lab = "simNumber",
  sim.evct.lab = "counter",
  probs = c(0.025, 0.975),
  add.baseline = T,
  add.last.area = T,
  subset = NULL,
  main = "Default",
  main.sub = "Default",
  main.sub.cex = 0.8,
  nbins = NULL,
  real.type = "l",
  real.lty = 1,
  real.lwd = 1,
  real.col = "blue",
  real.se = if (!is.null(sim.data)) F else T,
  real.se.type = "l",
  real.se.lty = 2,
  real.se.lwd = 0.5,
  real.se.col = "red",
  cens.type = "l",
  cens.lty = 1,
  cens.col = "black",
  cens.lwd = 1,
  cens.rll = 0.02,
  inclZeroWRES = TRUE,
  onlyfirst = FALSE,
  samp = NULL,
  poly.alpha = 1,
  poly.fill = "lightgreen",
  poly.line.col = "darkgreen",
  poly.lty = 2,
  censor.lines = TRUE,
  ylim = c(-5, 105),
  cov = NULL,
  cov.fun = "mean",
  ...
)

Arguments

`x`	The independent variable.
`y`	The dependent variable. event (>0) or no event (0).
`id`	The ID variable in the dataset.
`data`	A dataset can be used instead of the data in an Xpose object. Must have the same form as an xpose data object `xpdb@Data`.
`evid`	The EVID data item. If not present then all rows are considered events (can be censored or an event). Otherwise, EVID!=0 are dropped from the data set.
`by`	A vector of conditioning variables.
`xlab`	X-axis label
`ylab`	Y-axis label
`object`	An Xpose object. Needed if no `data` is supplied.
`events.to.plot`	Vector of events to be plotted. "All" means that all events are plotted.
`sim.data`	The simulated data file. Should be a table file with one header row and have, at least, columns with headers corresponding to `x`, `y`, `id`, `by` (if used), `nsim.lab` and `sim.evct.lab`.
`sim.zip.file`	The `sim.data` can be in \.zip format and xpose will unzip the file before reading in the data. Must have the same structure as described above in `sim.data`.
`VPC`	`TRUE` or `FALSE`. If `TRUE` then Xpose will search for a zipped file with name `paste("simtab",object@Runno,".zip",sep="")`, for example "simtab42.zip".
`nsim.lab`	The column header for `sim.data` that contains the simulation number for that row in the data.
`sim.evct.lab`	The column header for `sim.data` that contains the individual event counter information. For each individual the event counter should increase by one for each event (or censored event) that occurs.
`probs`	The probabilities (non-parametric percentiles) to use in computation of the prediction intervals for the simulated data.
`add.baseline`	Should a (x=0,y=1) baseline measurement be added to each individual in the dataset. Otherwise each plot will begin at the first event in the dataset.
`add.last.area`	Should an area be added to the VPC extending the last PI?
`subset`	The subset of the data and sim.data to use.
`main`	The title of the plot. Can also be `NULL` or `"Default"`.
`main.sub`	The title of the subplots. Must be a list, the same length as the number of subplots (actual graphs), or `NULL` or `"Default"`.
`main.sub.cex`	The size of the title of the subplots.
`nbins`	The number of bins to use in the VPC. If `NULL`, the the number of unique `x` values in `sim.data` is used.
`real.type`	Type for the real data.
`real.lty`	Line type (lty) for the curve of the original (or real) data.
`real.lwd`	Line width (lwd) for the real data.
`real.col`	Color for the curve of the original (or real) data.
`real.se`	Should the standard errors of the real (non simulated) data be plotted? Calculated using `survfit`.
`real.se.type`	Type for the standard errors.
`real.se.lty`	Line type (lty) for the standard error lines.
`real.se.lwd`	Line width (lwd) for the standard error lines.
`real.se.col`	Color for the standard error lines.
`cens.type`	Type for the censored lines.
`cens.lty`	Line type (lty) for the censored lines.
`cens.col`	Color for the censored lines.
`cens.lwd`	Line width for the censored lines.
`cens.rll`	The relative line length of the censored line compared to the limits of the y-axis.
`inclZeroWRES`	Include WRES=0 rows from the real data set in the plots?
`onlyfirst`	Include only the first measurement for the real data in the plots?
`samp`	Simulated data in the xpose data object can be used as the "real" data. `samp` is a number selecting which simulated data set to use.
`poly.alpha`	The transparency of the VPC shaded region.
`poly.fill`	The fill color of the VPC shaded region.
`poly.line.col`	The line colors for the VPC region.
`poly.lty`	The line type for the VPC region.
`censor.lines`	Should censored observations be marked on the plot?
`ylim`	Limits for the y-axes
`cov`	The covariate in the dataset to plot instead of the survival curve.
`cov.fun`	The summary function for the covariate in the dataset to plot instead of the survival curve.
`...`	Additional arguments passed to the function.

Value

returns an object of class "xpose.multiple.plot".

Author(s)

Andrew C. Hooker

Examples


## Not run: 
library(xpose4)

## Read in the data
runno <- "57"
xpdb <- xpose.data(runno)

####################################
# here are the real data plots
####################################

kaplan.plot(x="TIME",y="DV",object=xpdb)
kaplan.plot(x="TIME",y="DV",object=xpdb,
            events.to.plot=c(1,2),
            by=c("DOSE==0","DOSE!=0"))
kaplan.plot(x="TIME",y="DV",object=xpdb,
            events.to.plot=c(1,2),
            by=c("DOSE==0","DOSE==10",
            "DOSE==50","DOSE==200"))

## make a PDF of the plots
pdf(file=paste("run",runno,"_kaplan.pdf",sep=""))
kaplan.plot(x="TIME",y="DV",object=xpdb,
            by=c("DOSE==0","DOSE==10",
            "DOSE==50","DOSE==200"))
dev.off()

####################################
## VPC plots
####################################

kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,events.to.plot=c(1))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            events.to.plot=c(1,2,3),
            by=c("DOSE==0","DOSE!=0"))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            events.to.plot=c(1),
            by=c("DOSE==0","DOSE==10","DOSE==50","DOSE==200"))

## make a PDF of all plots
pdf(file=paste("run",runno,"_kaplan.pdf",sep=""))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            by=c("DOSE==0","DOSE==10","DOSE==50","DOSE==200"))
dev.off()

## End(Not run)

## Not run: 
library(xpose4)

## Read in the data
runno <- "57"
xpdb <- xpose.data(runno)

####################################
# here are the real data plots
####################################

kaplan.plot(x="TIME",y="DV",object=xpdb)
kaplan.plot(x="TIME",y="DV",object=xpdb,
            events.to.plot=c(1,2),
            by=c("DOSE==0","DOSE!=0"))
kaplan.plot(x="TIME",y="DV",object=xpdb,
            events.to.plot=c(1,2),
            by=c("DOSE==0","DOSE==10",
            "DOSE==50","DOSE==200"))

## make a PDF of the plots
pdf(file=paste("run",runno,"_kaplan.pdf",sep=""))
kaplan.plot(x="TIME",y="DV",object=xpdb,
            by=c("DOSE==0","DOSE==10",
            "DOSE==50","DOSE==200"))
dev.off()

####################################
## VPC plots
####################################

kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,events.to.plot=c(1))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            events.to.plot=c(1,2,3),
            by=c("DOSE==0","DOSE!=0"))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            events.to.plot=c(1),
            by=c("DOSE==0","DOSE==10","DOSE==50","DOSE==200"))

## make a PDF of all plots
pdf(file=paste("run",runno,"_kaplan.pdf",sep=""))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            by=c("DOSE==0","DOSE==10","DOSE==50","DOSE==200"))
dev.off()

## End(Not run)

Make stacked bar data set.

Description

Function to make stacked bar data set for categorical data plots.

Usage

make.sb.data(data, idv, dv, nbins = 6, by = NULL, by.nbins = 6, ...)
make.sb.data(data, idv, dv, nbins = 6, by = NULL, by.nbins = 6, ...)

Arguments

`data`	Data set to transform.
`idv`	the independent variable.
`dv`	the dependent variable.
`nbins`	the number of bins.
`by`	Conditioning variable.
`by.nbins`	by.nbins.
`...`	additional arguments.

Author(s)

The Xpose team.

Function to plot the coverage of the Numerical Predictive Check

Description

This function takes the output from the npc command in Perl Speaks NONMEM (PsN) and makes a coverage plot. A coverage plot for the NPC looks at different prediction intervals (PIs) for each data point and calculates the total number of data points in the data set lying outside of these PIs. The plot shows the relative amount of data points outside of their PI compared to the expected amount at that PI. In addition a confidence interval around these values are computed based on the simulated data.

Usage

npc.coverage(
  npc.info = "npc_results.csv",
  main = "Default",
  main.sub = NULL,
  main.sub.cex = 0.85,
  ...
)
npc.coverage(
  npc.info = "npc_results.csv",
  main = "Default",
  main.sub = NULL,
  main.sub.cex = 0.85,
  ...
)

Arguments

`npc.info`	The results file from the `npc` command in PsN. For example, ‘npc_results.csv’, or if the file is in a separate directory ‘./npc_dir1/npc_results.csv’.
`main`	A string giving the plot title or `NULL` if none. `"Default"` creates a default title.
`main.sub`	Used for names above each plot when using multiple plots. Should be a vector `c("Group 1","Group 2")`
`main.sub.cex`	The size of the `main.sub` titles.
`...`	Other arguments passed to `xpose.multiple.plot.default`, `xyplot` and others. Please see these functions (and below) for more descriptions of what you can do.

Value

A list of plots

Additional arguments for the NPC coverage plots

Additional plot features

CI: Specifies whether confidence intervals (as lines, a shaded area or both) should be added to the plot. Allowed values are: "area", "lines", "both", or NULL.
mark.outside.data: Should the points outside the CI be marked in a different color to identify them. Allowed values are TRUE or FALSE.
abline: Should there be a line to mark the value of y=1? Possible values are TRUE, FALSE and NULL.

Line and area control. See plot, grid.polygon and xyplot for more details.

CI.area.col: Color of the area for the CI. Defaults to "blue"
CI.area.alpha: Transparency of the CI.area.col. Defaults to 0.3.
ab.lwd: The width of the abline. Default is 1.
ab.lty: Line type of the abline. Default is "dashed"
CI.upper.lty: Line type of the line at the upper edge of the CI.
CI.upper.col: Color of the line at the upper edge of the CI.
CI.upper.lwd: The line width of the line at the upper edge of the CI.
CI.lower.lty: The line type at the lower edge of the CI.
CI.lower.col: The color of the line at the lower edge of the CI.
CI.lower.lwd: The line width of the line at the lower edge of the CI.
obs.col: The color of the observed values.
obs.pch: The type of point to use for the observed values.
obs.lty: The type of line to use for the observed values.
obs.type: The combination of lines and points to use for the observed values. Default is "b" for both.
obs.cex: The size of the points to use for the observed values.
obs.lwd: The line width to use for the observed values.
out.col: The color of the observed values that lie outside of the CI. Only used if mark.outside.data=TRUE.
out.pch: The type of point to use for the observed values that lie outside of the CI. Only used if mark.outside.data = TRUE.
out.cex: The size of the points of the observed values that lie outside of the CI. Only used if mark.outside.data = TRUE.
out.lwd: The line width of the observed values that lie outside of the CI. Only used if mark.outside.data = TRUE.

Author(s)

Andrew Hooker

Examples


## Not run: 
library(xpose4)

npc.coverage()

## to read files in a directory different than the current working directory 
npc.file <- "./another_directory/npc_results.csv"
npc.coverage(npc.info=npc.file)

## End(Not run)

## Not run: 
library(xpose4)

npc.coverage()

## to read files in a directory different than the current working directory 
npc.file <- "./another_directory/npc_results.csv"
npc.coverage(npc.info=npc.file)

## End(Not run)

Extract or set the value of the Nsim slot.

Description

Extract or set the value of the Nsim slot of an "xpose.data" object.

Usage

nsim(object)
nsim(object)

Arguments

object

An "xpose.data" object.

Author(s)

Niclas Jonsson

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Report number of simulations
nsim(xpdb5)

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Report number of simulations
nsim(xpdb5)

## End(Not run)

Plot the parameter or covariate distributions using a histogram

Description

These functions plot the parameter or covariate values stored in an Xpose data object using histograms.

Usage

cov.hist(object, onlyfirst = TRUE, main = "Default", ...)

parm.hist(object, onlyfirst = TRUE, main = "Default", ...)

ranpar.hist(object, onlyfirst = TRUE, main = "Default", ...)
cov.hist(object, onlyfirst = TRUE, main = "Default", ...)

parm.hist(object, onlyfirst = TRUE, main = "Default", ...)

ranpar.hist(object, onlyfirst = TRUE, main = "Default", ...)

Arguments

`object`	An xpose.data object.
`onlyfirst`	Logical value indicating if only the first row per individual is included in the plot.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `xpose.plot.histogram`.

Details

Each of the parameters or covariates in the Xpose data object, as specified in object@Prefs@Xvardef$parms, object@Prefs@Xvardef$covariates or object@Prefs@Xvardef$ranpar is evaluated in turn, creating a stack of histograms.

A wide array of extra options controlling histograms are available. See xpose.plot.histogram for details.

Value

Delivers a stack of histograms.

Functions

cov.hist(): Covariate distributions
parm.hist(): parameter distributions
ranpar.hist(): random parameter distributions

Author(s)

Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## Parameter histograms
parm.hist(xpdb)

## Covariate distribution, in green
cov.hist(xpdb, hicol=11, hidcol="DarkGreen", hiborder="White")

## Random parameter histograms
ranpar.hist(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## Parameter histograms
parm.hist(xpdb)

## Covariate distribution, in green
cov.hist(xpdb, hicol=11, hidcol="DarkGreen", hiborder="White")

## Random parameter histograms
ranpar.hist(xpdb)

Plot the parameter or covariate distributions using quantile-quantile (Q-Q) plots

Description

These functions plot the parameter or covariate values stored in an Xpose data object using Q-Q plots.

Usage

cov.qq(object, onlyfirst = TRUE, main = "Default", ...)

parm.qq(object, onlyfirst = TRUE, main = "Default", ...)

ranpar.qq(object, onlyfirst = TRUE, main = "Default", ...)
cov.qq(object, onlyfirst = TRUE, main = "Default", ...)

parm.qq(object, onlyfirst = TRUE, main = "Default", ...)

ranpar.qq(object, onlyfirst = TRUE, main = "Default", ...)

Arguments

`object`	An xpose.data object.
`onlyfirst`	Logical value indicating if only the first row per individual is included in the plot.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `xpose.plot.qq`.

Details

Each of the parameters or covariates in the Xpose data object, as specified in object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar or object@Prefs@Xvardef$covariates, is evaluated in turn, creating a stack of Q-Q plots.

A wide array of extra options controlling Q-Q plots are available. See xpose.plot.qq for details.

Value

Delivers a stack of Q-Q plots.

Functions

cov.qq(): Covariate distributions
parm.qq(): parameter distributions
ranpar.qq(): random parameter distributions

Author(s)

Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## parameter histograms
parm.qq(xpdb)

## A stack of random parameter histograms
ranpar.qq(xpdb)

## Covariate distribution, in green with red line of identity
cov.qq(xpdb, col=11, ablcol=2)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## parameter histograms
parm.qq(xpdb)

## A stack of random parameter histograms
ranpar.qq(xpdb)

## Covariate distribution, in green with red line of identity
cov.qq(xpdb, col=11, ablcol=2)

Summarize individual parameter values and covariates

Description

These functions produce tables, printed to the screen, summarizing the individual parameter values or covariates from a dataset in Xpose 4.

Usage

cov.summary(
  object,
  onlyfirst = TRUE,
  subset = xsubset(object),
  inclZeroWRES = FALSE,
  out.file = ".screen",
  main = "Default",
  fill = "gray",
  values.to.use = xvardef("covariates", object),
  value.name = "Covariate",
  ...
)

parm.summary(
  object,
  onlyfirst = TRUE,
  subset = xsubset(object),
  inclZeroWRES = FALSE,
  out.file = ".screen",
  main = "Default",
  fill = "gray",
  values.to.use = xvardef("parms", object),
  value.name = "Parameter",
  max.plots.per.page = 1,
  ...
)
cov.summary(
  object,
  onlyfirst = TRUE,
  subset = xsubset(object),
  inclZeroWRES = FALSE,
  out.file = ".screen",
  main = "Default",
  fill = "gray",
  values.to.use = xvardef("covariates", object),
  value.name = "Covariate",
  ...
)

parm.summary(
  object,
  onlyfirst = TRUE,
  subset = xsubset(object),
  inclZeroWRES = FALSE,
  out.file = ".screen",
  main = "Default",
  fill = "gray",
  values.to.use = xvardef("parms", object),
  value.name = "Parameter",
  max.plots.per.page = 1,
  ...
)

Arguments

`object`	An xpose.data object.
`onlyfirst`	Logical value indicating if only the first row per individual is included in the plot.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 are included in the plot. The default is FALSE.
`out.file`	Where the results should be output to. Can be ".screen", ".ask", ".graph" or a filename in quotes.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`fill`	The color to fill the boxes in the table if the table is printed to ".graph"
`values.to.use`	Which values should be summarized
`value.name`	The name of the values
`...`	Other arguments passed to `Data` and `SData`.
`max.plots.per.page`	Maximum plots per page.

Value

Returned is the matrix of values from the table. parm.summary and cov.summary produce summaries of parameters and covariates, respectively. parm.summary produces less attractive output but supports mirror functionality.

parm.summary and cov.summary utilize print.char.matrix to print the information to the screen.

Functions

cov.summary(): Covariate summary
parm.summary(): Parameter summary

Author(s)

Andrew Hooker & Justin Wilkins

Examples


parm.summary(simpraz.xpdb)

parm.summary(simpraz.xpdb)

Parameters plotted against covariates, for Xpose 4

Description

This creates a stack of plots of Bayesian parameter estimates plotted against covariates, and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

parm.vs.cov(
  object,
  onlyfirst = TRUE,
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)
parm.vs.cov(
  object,
  onlyfirst = TRUE,
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`type`	The plot type - defaults to points only.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the parameters in the Xpose data object, as specified in object@Prefs@Xvardef$parms, is plotted against each covariate present, as specified in object@Prefs@Xvardef$covariates, creating a stack of plots.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots and histograms of parameters against covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)

## A vanilla plot
parm.vs.cov(xpdb)

## Custom colours and symbols, IDs
parm.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)

## A vanilla plot
parm.vs.cov(xpdb)

## Custom colours and symbols, IDs
parm.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

Plot parameters vs other parameters

Description

This function plots the parameter values stored in an Xpose data object versus each other in a series of graphs. The mirror functionality is available for this function.

Usage

parm.vs.parm(
  object,
  onlyfirst = TRUE,
  abline = FALSE,
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)
parm.vs.parm(
  object,
  onlyfirst = TRUE,
  abline = FALSE,
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`abline`	Allows for a line of identity.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`type`	The plot type - defaults to points only.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `xpose.plot.default`.

Details

Each of the parameters in the Xpose data object, as specified in object@Prefs@Xvardef$parms, is plotted against the rest, creating a stack of plots.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots and histograms of parameters against parameters.

Author(s)

Andrew Hooker

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)


parm.vs.parm(xpdb)


parm.vs.parm(xpdb,mirror=3)

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)


parm.vs.parm(xpdb)


parm.vs.parm(xpdb,mirror=3)

## End(Not run)

Population predictions (PRED) plotted against the independent variable (IDV) for Xpose 4

Description

This is a plot of population predictions (PRED) vs the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

pred.vs.idv(object, smooth = TRUE, ...)
pred.vs.idv(object, smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of PRED vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

pred.vs.idv(xpdb)

## A conditioning plot
pred.vs.idv(xpdb, by="HCTZ")

## Logarithmic Y-axis
pred.vs.idv(xpdb, logy=TRUE)

## Custom colours and symbols, IDs
pred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

pred.vs.idv(xpdb)

## A conditioning plot
pred.vs.idv(xpdb, by="HCTZ")

## Logarithmic Y-axis
pred.vs.idv(xpdb, logy=TRUE)

## Custom colours and symbols, IDs
pred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

Print an Xpose multiple plot object.

Description

Print an Xpose multiple plot object, which is the output from the function xpose.multiple.plot.

Usage

## S3 method for class 'xpose.multiple.plot'
print(x, ...)
## S3 method for class 'xpose.multiple.plot'
print(x, ...)

Arguments

`x`	Output object from the function `xpose.multiple.plot`.
`...`	Additional options passed to function.

Details

Print method for a plot class.

Author(s)

Niclas Jonsson and Andrew C. Hooker

Function to create a histogram of results from the randomization test tool (`randtest`) in PsN

Description

Reads results from the randtest tool in PsN and then creates a histogram.

Usage

randtest.hist(
  results.file = "raw_results_run1.csv",
  df = 1,
  p.val = 0.05,
  main = "Default",
  xlim = NULL,
  PCTSlcol = "black",
  vlcol = c("red", "orange"),
  ...
)
randtest.hist(
  results.file = "raw_results_run1.csv",
  df = 1,
  p.val = 0.05,
  main = "Default",
  xlim = NULL,
  PCTSlcol = "black",
  vlcol = c("red", "orange"),
  ...
)

Arguments

`results.file`	The location of the results file from the `randtest` tool in PsN
`df`	The degrees of freedom between the full and reduced model used in the randomization test.
`p.val`	The p-value you would like to use.
`main`	The title of the plot.
`xlim`	The limits of the x-axis
`PCTSlcol`	Color of the empirical line
`vlcol`	Colors of the original and nominal line
`...`	Additional arguments that can be passed to xpose.plot.histogram, xpose.panel.histogram, histogram and other lattice-package functions.

Value

A lattice object

Author(s)

Andrew Hooker

References

PsN

Examples


## Not run: 
randtest.hist(results.file="randtest_dir1/raw_results_run1.csv",df=2)

## End(Not run)

## Not run: 
randtest.hist(results.file="randtest_dir1/raw_results_run1.csv",df=2)

## End(Not run)

Random parameters plotted against covariates, for Xpose 4

Description

This creates a stack of plots of Bayesian random parameter estimates plotted against covariates, and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

ranpar.vs.cov(
  object,
  onlyfirst = TRUE,
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)
ranpar.vs.cov(
  object,
  onlyfirst = TRUE,
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`type`	The plot type - defaults to points only.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Each of the random parameters (ETAs) in the Xpose data object, as specified in object@Prefs@Xvardef$ranpar, is plotted against each covariate present, as specified in object@Prefs@Xvardef$covariates, creating a stack of plots.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns a stack of xyplots and histograms of random parameters against covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)

## A vanilla plot
ranpar.vs.cov(xpdb)

## Custom colours and symbols, IDs
ranpar.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)

## A vanilla plot
ranpar.vs.cov(xpdb)

## Custom colours and symbols, IDs
ranpar.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

Read NONMEM output files into Xpose 4

Description

These are functions that read in a NONMEM output file (a '*.lst' file) and then format the input.

Usage

calc.npar(object)

create.parameter.list(listfile)

read.lst(filename)
calc.npar(object)

create.parameter.list(listfile)

read.lst(filename)

Arguments

`object`	The return value of `read.lst(filename)`
`listfile`	A NONMEM output file.
`filename`	A NONMEM output file.

Value

lists of read values.

Functions

calc.npar(): calculates the number and type of parameters included in a NONMEM output file
create.parameter.list(): Reads parameters, uncertainty and termination messages included in a NONMEM output file
read.lst(): parses information out of NONMEM output.

Author(s)

Niclas Jonsson, Andrew Hooker & Justin Wilkins

Read NONMEM table files produced from simulation.

Description

The function reads in NONMEM table files produced from the $SIM line in a NONMEM model file.

Usage

read_nm_table(
  nm_table,
  only_obs = FALSE,
  method = "default",
  quiet = TRUE,
  sim_num = FALSE,
  sim_name = "NSIM"
)
read_nm_table(
  nm_table,
  only_obs = FALSE,
  method = "default",
  quiet = TRUE,
  sim_num = FALSE,
  sim_name = "NSIM"
)

Arguments

`nm_table`	The NONMEM table file to read. A text string.
`only_obs`	Should the non-observation lines in the data set be removed? Currently filtered using the expected `MDV` column. `TRUE` or `FALSE`.
`method`	The methods to use for reading the tables, Can be "readr_1", "readr_2", readr_3" or "slow".
`quiet`	Should the error message be verbose or not?
`sim_num`	Should a simulation number be added to simulation tables?
`sim_name`	What name should one use to name the column of the simulation number?

Details

Currently the function expects the $TABLE to have a header for each new simulation. This means that the NOHEADER option or ONEHEADER option in the table file is not allowed.

Value

Returns a data frame of the simulated table with an added column for the simulation number. The data frame is given class c("tbl_df", "tbl", "data.frame") for easy use with dplyr.

Reading NONMEM table files

Description

Reads one or more NONMEM table files, removes duplicated columns and merges the data into a data.frame.

Usage

read.nm.tables(
  table.files = NULL,
  runno = NULL,
  tab.suffix = "",
  table.names = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab"),
  cwres.name = c("cwtab"),
  cwres.suffix = "",
  quiet = FALSE,
  new_methods = TRUE,
  ...
)
read.nm.tables(
  table.files = NULL,
  runno = NULL,
  tab.suffix = "",
  table.names = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab"),
  cwres.name = c("cwtab"),
  cwres.suffix = "",
  quiet = FALSE,
  new_methods = TRUE,
  ...
)

Arguments

`table.files`	Exact names of table files to read. If not provided then the exact names are created using the other arguments to the function.
`runno`	Run-number to identify sets of table files.
`tab.suffix`	Table file name suffix.
`table.names`	Vector of template table file names to read.
`cwres.name`	Vector of CWRES table file names to read.
`cwres.suffix`	CWRES table file name suffix.
`quiet`	Logical value to indicate whether some warnings should be quiet or not.
`new_methods`	Should faster methods of reading tables be used (uses readr package)?
`...`	Additional arguments passed to this function

Details

Reads one or more table files, removes duplicate columns and merges the data. The function also checks to see if the table files are of the same length (required).

If there are header lines in the table files (for example if your data are simulated with NSUB>1), these are removed.

The table file names to read are constructed from the file name templates of table.names. The runno and tab.suffix are appended to the file name template before checking if the file is readable.

Xpose expects, by default, to find the following NONMEM tables in the working directory to be able to create an Xpose data object (using a run number of 5 as an example):

sdtab5: The 'standard' parameters, including IWRE, IPRE, TIME, and the NONMEM default items (DV, PRED, RES and WRES) that are added when NOAPPEND is not present in the $TABLE record.

$TABLE ID TIME IPRE IWRE NOPRINT ONEHEADER FILE=sdtab5

patab5: The empirical Bayes estimates of individual model parameter values, or posthoc estimates. These are model parameters, such as CL, V2, ETA1, etc.

$TABLE ID CL V2 KA K F1 ETA1 ETA2 ETA3 NOPRINT NOAPPEND ONEHEADER FILE=patab5

catab5: Categorical covariates, e.g. SEX, RACE.

$TABLE ID SEX HIV GRP NOPRINT NOAPPEND ONEHEADER FILE=catab5

cotab5: Continuous covariates, e.g. WT, AGE.

$TABLE ID WT AGE BSA HT GGT HB NOPRINT NOAPPEND ONEHEADER FILE=cotab5

mutab5, mytab5, extra5, xptab5: Additional variables of any kind. These might be useful if there are more covariates than can be accommodated in the covariates tables, for example, or if you have other variables that should be added, e.g. CMAX, AUC.

Value

A dataframe.

Author(s)

Niclas Jonsson, Andrew Hooker

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory, and that the table files have
## a suffix of '.dat', e.g. sdtab5.dat

my.dataframe <- read.nm.tables(5, tab.suffix = ".dat") 

## End(Not run)



## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory, and that the table files have
## a suffix of '.dat', e.g. sdtab5.dat

my.dataframe <- read.nm.tables(5, tab.suffix = ".dat") 

## End(Not run)

Read the results file from a Numerical or Visual Predictive Check run in PsN

Description

This function reads in a results file from running either the PsN command vpc or npc. The function then parses the file and passes the result to plotting functions.

Usage

read.npc.vpc.results(
  vpc.results = NULL,
  npc.results = NULL,
  verbose = FALSE,
  ...
)
read.npc.vpc.results(
  vpc.results = NULL,
  npc.results = NULL,
  verbose = FALSE,
  ...
)

Arguments

`vpc.results`	The name of the results file from running the PsN command `vcp`. Often this is named ‘vpc_results.csv’. If the file is in a directory different then the working directory then you can define a relative or absolute path to the file by, for example, ‘./vpc_strat_WT_4_mirror_5/vpc_results.csv’.
`npc.results`	The name of the results file from running the PsN command `npc`. Often this is named ‘npc_results.csv’. relative or absolute paths to the file are allowed as for `vpc.results`.
`verbose`	Text messages passed to screen or not.
`...`	arguments passed to other functions.

Details

One of vpc.results or npc.results are necessary. If both or none are defined then the function does nothing and a NULL is returned from the function.

Value

A list of values is returned.

`model.file`	The model file that PsN ran either the `npc` or `vpc` with
`dv.var`	The dependent variable used in the calculations.
`idv.var`	The independent variable used in the calculations. `NULL` if `npc.results` is used.
`num.tables`	The number of separate tables in the results file.
`by.interval`	The conditioning interval for the stratification variable, only returned if `vpc.results` is used.
`result.tables`	The results tables from the results file. this is a list.

Author(s)

Andrew Hooker

Read (repeated) time-to-event simulation data files.

Description

Read (repeated) time-to-event simulation data files.

Usage

read.TTE.sim.data(
  sim.file,
  subset = NULL,
  headers = c("REP", "ID", "DV", "TIME", "FLAG2", "DOSE"),
  xpose.table.file = FALSE,
  ...
)
read.TTE.sim.data(
  sim.file,
  subset = NULL,
  headers = c("REP", "ID", "DV", "TIME", "FLAG2", "DOSE"),
  xpose.table.file = FALSE,
  ...
)

Arguments

`sim.file`	Name of the simulated file.
`subset`	subset to extract.
`headers`	headers in file.
`xpose.table.file`	xpose table files.
`...`	Extra arguments passed to function.

Author(s)

Andrew C. Hooker

Read the vpctab file from PsN into Xpose

Description

This function read in the vpctab file created from PsN and gathers the information needed to make a vpc plot.

Usage

read.vpctab(
  vpctab = NULL,
  object = NULL,
  vpc.name = "vpctab",
  vpc.suffix = "",
  tab.suffix = "",
  inclZeroWRES = FALSE,
  verbose = FALSE,
  ...
)
read.vpctab(
  vpctab = NULL,
  object = NULL,
  vpc.name = "vpctab",
  vpc.suffix = "",
  tab.suffix = "",
  inclZeroWRES = FALSE,
  verbose = FALSE,
  ...
)

Arguments

`vpctab`	The vpctab file from a '`vpc`' run in PsN.
`object`	An xpose data object. Created from `xpose.data`. One of `object` or `vpctab` is required. If both are present then the information from the `vpctab` will over-ride the xpose data object `object` (i.e. the values from the vpctab will replace any matching values in the `object@Data` portion of the xpose data object). If only `object` is present then the function will look for a vpctab with the same run number as the one associated with the object.
`vpc.name`	The default name of the vpctab file. Used if only `object` is supplied.
`vpc.suffix`	The suffix of the vpctab file. Used if only `object` is supplied.
`tab.suffix`	The table suffix of the vpctab file. Used if only `object` is supplied. Final order of the file would be then `paste(vpc.name,object@Runno,vpc.suffix,tab.suffix)`
`inclZeroWRES`	If there are no zero valued weighted residuals in the `object` then this should be `TRUE`.
`verbose`	Text messages passed to screen or not.
`...`	Other arguments passed to other functions.

Value

Returned is an xpose data object with vpctab information included.

Author(s)

Andrew Hooker

Resets Xpose variable definitions to factory settings

Description

Function to reset Xpose's graphics parameters definitions to the default.

Usage

reset.graph.par(object, classic = FALSE)
reset.graph.par(object, classic = FALSE)

Arguments

`object`	An `xpose.data` object.
`classic`	A logical operator specifying whether the function should assume the classic menu system. This is an internal option and need never be called from the command line.

Details

This functions is used to reset Xpose's graphic settings definitions to their default values. Graphical settings are read from the file 'xpose.ini' in the root of the 'xpose4' package.

Value

An xpose.data object (classic == FALSE) or null (classic == TRUE).

Author(s)

Niclas Jonsson & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Import graphics preferences you saved earlier using export.graph.par
xpdb5 <- import.graph.par(xpdb5)

## Reset to default values
xpdb5 <- reset.graph.par(xpdb5)

## Change WRES definition
xpdb5 <- change.wres(xpdb5)

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Import graphics preferences you saved earlier using export.graph.par
xpdb5 <- import.graph.par(xpdb5)

## Reset to default values
xpdb5 <- reset.graph.par(xpdb5)

## Change WRES definition
xpdb5 <- change.wres(xpdb5)

## End(Not run)

Print run summary in Xpose 4

Description

Function to build Xpose run summaries.

Usage

runsum(
  object,
  dir = "",
  modfile = paste(dir, "run", object@Runno, ".mod", sep = ""),
  listfile = paste(dir, "run", object@Runno, ".lst", sep = ""),
  main = NULL,
  subset = xsubset(object),
  show.plots = TRUE,
  txt.cex = 0.7,
  txt.font = 1,
  show.ids = FALSE,
  param.table = TRUE,
  txt.columns = 2,
  force.wres = FALSE,
  ...
)
runsum(
  object,
  dir = "",
  modfile = paste(dir, "run", object@Runno, ".mod", sep = ""),
  listfile = paste(dir, "run", object@Runno, ".lst", sep = ""),
  main = NULL,
  subset = xsubset(object),
  show.plots = TRUE,
  txt.cex = 0.7,
  txt.font = 1,
  show.ids = FALSE,
  param.table = TRUE,
  txt.columns = 2,
  force.wres = FALSE,
  ...
)

Arguments

`object`	An xpose.data object.
`dir`	The directory to look for the model and output file of a NONMEM run.
`modfile`	The name of the NONMEM control stream associated with the current run.
`listfile`	The name of the NONMEM output file associated with the current run.
`main`	A string giving the main heading. `NULL` if none.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`show.plots`	Logical indicating if GOF plots should be shown in the run summary.
`txt.cex`	Number indicating the size of the txt in the run summary.
`txt.font`	Font of the text in the run summary.
`show.ids`	Logical indicating if IDs should be plotted in the plots for the run summary.
`param.table`	Logical indicating if the parameter table should be shown in the run summary.
`txt.columns`	The number of text columns in the run summary.
`force.wres`	Plot the WRES even if other residuals are available.
`...`	Other arguments passed to the various functions.

Value

A compound plot containing an Xpose run summary is created.

Author(s)

Niclas Jonsson and Andrew Hooker

Examples

od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

xpdb <- xpose.data(1)
runsum(xpdb)


file.remove(new.files) # remove these files
setwd(od)  # restore working directory


od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

xpdb <- xpose.data(1)
runsum(xpdb)


file.remove(new.files) # remove these files
setwd(od)  # restore working directory

Simulated prazosin Xpose database.

Description

Xpose database from the NONMEM output of a model for prazosin using simulated data (and NONMEM 7.3).

Usage

simpraz.xpdb
simpraz.xpdb

Format

an xpose.data object

Details

The database can be used to test functions in Xpose 4. This database is slightly different than the database that is created when reading in the files created by simprazExample using xpose.data.

Examples


xpose.print(simpraz.xpdb)
Data(simpraz.xpdb)
str(simpraz.xpdb)

xpose.print(simpraz.xpdb)
Data(simpraz.xpdb)
str(simpraz.xpdb)

Function to create files for the simulated prazosin example in Xpose

Description

Creates NONMEM data, model and output files for a model of prazosin using simulated data.

Usage

simprazExample(overwrite = FALSE)
simprazExample(overwrite = FALSE)

Arguments

overwrite

Logical. Should the function overwrite files with the same names already in the current working directory?

Details

Creates files in the current working directory named: run1.ext run1.lst run1.mod simpraz.dta xptab1

Author(s)

Niclas Jonsson and Andrew Hooker

Examples


od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files

(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

file.remove(new.files) # remove these files
setwd(od)  # restore working directory

od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files

(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

file.remove(new.files) # remove these files
setwd(od)  # restore working directory

Tabulate the population parameter estimates

Description

This function provides a summary of the model's parameter estimates and precision.

Usage

tabulate.parameters(object, prompt = FALSE, outfile = NULL, dir = "")
tabulate.parameters(object, prompt = FALSE, outfile = NULL, dir = "")

Arguments

`object`	An xpose.data object.
`prompt`	Ask before printing.
`outfile`	file to output to (NULL means screen).
`dir`	Which directory is the NONMEM output file located. `""` means the current working directory `getwd()`.

Value

A table summarizing the parameters and their precision.

Author(s)

Niclas Jonsson, Andrew Hooker & Justin Wilkins

Examples


od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?
xpdb <- xpose.data(1) # read in files to xpose database

tabulate.parameters(xpdb)

file.remove(new.files) # remove these files
setwd(od)  # restore working directory

od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?
xpdb <- xpose.data(1) # read in files to xpose database

tabulate.parameters(xpdb)

file.remove(new.files) # remove these files
setwd(od)  # restore working directory

Histogram of weighted residuals (WRES), for Xpose 4

Description

This is a histogram of the distribution of weighted residuals (WRES) in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.histogram function.

Usage

wres.dist.hist(object, ...)
wres.dist.hist(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `xpose.plot.histogram`.

Details

Displays a histogram of the weighted residuals (WRES).

Value

Returns a histogram of weighted residuals (WRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.dist.hist(xpdb)


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.dist.hist(xpdb)

Quantile-quantile plot of weighted residuals (WRES), for Xpose 4

Description

This is a QQ plot of the distribution of weighted residuals (WRES) in the dataset, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.qq function.

Usage

wres.dist.qq(object, ...)
wres.dist.qq(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.qq}`.

Details

Displays a QQ plot of the weighted residuals (WRES).

Value

Returns a QQ plot of weighted residuals (WRES).

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.dist.qq(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.dist.qq(xpdb)

Weighted residuals (WRES) plotted against covariates, for Xpose 4

Description

This creates a stack of plots of weighted residuals (WRES) plotted against covariates, and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default and xpose.plot.histogram functions. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

wres.vs.cov(
  object,
  ylb = "WRES",
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)
wres.vs.cov(
  object,
  ylb = "WRES",
  smooth = TRUE,
  type = "p",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`main`	The title of the plot. If `"Default"` then a default title is plotted. Otherwise the value should be a string like `"my title"` or `NULL` for no plot title.
`...`	Other arguments passed to `link{xpose.plot.default}` or `link{xpose.plot.histogram}`.

Details

Weighted residuals (WRES) are plotted against each covariate present, as specified in object@Prefs@Xvardef$covariates, creating a stack of plots.

A wide array of extra options controlling xyplots and histograms are available. See xpose.plot.default and xpose.plot.histogram for details.

Value

Returns a stack of xyplots and histograms of CWRES versus covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
wres.vs.cov(xpdb)

## Custom colours and symbols, IDs
wres.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
wres.vs.cov(xpdb)

## Custom colours and symbols, IDs
wres.vs.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)

## End(Not run)

Population weighted residuals (WRES) plotted against the independent variable (IDV) for Xpose 4

Description

This is a plot of population weighted residuals (WRES) vs the independent variable (IDV), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

wres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)
wres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)

Arguments

`object`	An xpose.data object.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

Weighted residuals (WRES) are plotted against the independent variable, as specified in object@Prefs@Xvardef$idv.

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of WRES vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.idv(xpdb)

## A conditioning plot
wres.vs.idv(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.idv(xpdb)

## A conditioning plot
wres.vs.idv(xpdb, by="HCTZ")

Box-and-whisker plot of weighted residuals vs the independent variable for Xpose 4

Description

This creates a box and whisker plot of weighted residuals (WRES) vs the independent variable (IDV), and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

wres.vs.idv.bw(object, ...)
wres.vs.idv.bw(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.bw}`.

Details

A wide array of extra options controlling bwplots are available. See xpose.plot.bw and xpose.panel.bw for details.

Value

Returns a stack of box-and-whisker plots of WRES vs IDV.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.idv.bw(xpdb)

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.idv.bw(xpdb)

Population weighted residuals (WRES) plotted against population predictions (PRED) for Xpose 4

Description

This is a plot of population weighted residuals (WRES) vs population predictions (PRED), a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

wres.vs.pred(object, smooth = TRUE, abline = c(0, 0), ...)
wres.vs.pred(object, smooth = TRUE, abline = c(0, 0), ...)

Arguments

`object`	An xpose.data object.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`...`	Other arguments passed to `link{xpose.plot.default}`.

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Value

Returns an xyplot of WRES vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.pred(xpdb)

## A conditioning plot
wres.vs.pred(xpdb, by="HCTZ")

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.pred(xpdb)

## A conditioning plot
wres.vs.pred(xpdb, by="HCTZ")

Box-and-whisker plot of weighted residuals vs population predictions for Xpose 4

Description

This creates a box and whisker plot of weighted residuals (WRES) vs population predictions (PRED), and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

wres.vs.pred.bw(object, ...)
wres.vs.pred.bw(object, ...)

Arguments

`object`	An xpose.data object.
`...`	Other arguments passed to `link{xpose.plot.bw}`.

Details

A wide array of extra options controlling bwplots are available. See xpose.plot.bw and xpose.panel.bw for details.

Value

Returns a box-and-whisker plot of WRES vs PRED.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.pred.bw(xpdb)


## Here we load the example xpose database 
xpdb <- simpraz.xpdb

wres.vs.pred.bw(xpdb)

Extract and set labels for Xpose data items.

Description

This function extracts and sets label definitions in Xpose data objects.

Usage

xlabel(x, object)

xlabel(object) <- value
xlabel(x, object)

xlabel(object) <- value

Arguments

`x`	Name of the variable to assign a label to.
`object`	An `xpose.data` object.
`value`	A two element vector of which the first element is the name of the variable and the second the label

Details

x should be a string exactly matching the name of a column in the data.frame in the Data slot of an xpose.data object. The name of columns defined through xpose variable definitions (see xpose.data) can be extracted using the xvardef function and to be used in the xlabel function, e.g. xlabel(xvardef("dv",object),object), which would give the label for the dv variable.

Value

The label of the specified column.

Functions

xlabel(object) <- value: sets label definitions in Xpose data objects. assigned value should be a two-element vector of which the first element is the name of the variable and the second the label

Author(s)

Niclas Jonsson

Examples


xpdb <- simpraz.xpdb

## Display label for dependent variable in the Xpose data object
xlabel("DV", xpdb)

## Set label for dependent variable
xlabel(xpdb) <- c("DV", "Concentration (mg/L)")
xlabel("DV", xpdb) # how has this chnaged?
xpdb <- simpraz.xpdb

## Display label for dependent variable in the Xpose data object
xlabel("DV", xpdb)

## Set label for dependent variable
xlabel(xpdb) <- c("DV", "Concentration (mg/L)")
xlabel("DV", xpdb) # how has this chnaged?

Compare parameter estimates for covariate coefficients

Description

This function creates a plot of the estimates for covariate coefficients, obtained from the first step (univariate testing) in each scm performed in the bootscm. When normalized for their standard deviation, these plots can be used to compare the strength of the covariate relationship. Coloring is based on the covariate being included in the final model (blue) not being included (red).

Usage

xp.boot.par.est(
  bootgam.obj = NULL,
  sd.norm = TRUE,
  by.cov.type = FALSE,
  abs.values = FALSE,
  show.data = TRUE,
  show.means = TRUE,
  show.bias = TRUE,
  dotpch = c(1, 19),
  labels = NULL,
  pch.mean = "|",
  xlab = NULL,
  ylab = NULL,
  col = c(rgb(0.8, 0.5, 0.5), rgb(0.2, 0.2, 0.7), rgb(0.2, 0.2, 0.7), rgb(0.6, 0.6, 0.6)),
  ...
)
xp.boot.par.est(
  bootgam.obj = NULL,
  sd.norm = TRUE,
  by.cov.type = FALSE,
  abs.values = FALSE,
  show.data = TRUE,
  show.means = TRUE,
  show.bias = TRUE,
  dotpch = c(1, 19),
  labels = NULL,
  pch.mean = "|",
  xlab = NULL,
  ylab = NULL,
  col = c(rgb(0.8, 0.5, 0.5), rgb(0.2, 0.2, 0.7), rgb(0.2, 0.2, 0.7), rgb(0.6, 0.6, 0.6)),
  ...
)

Arguments

`bootgam.obj`	The object created using bootscm.import(), which hold the data for plotting.
`sd.norm`	Perform normalization of the covariate coefficients (default is TRUE). When TRUE, the estimated covariate coefficients will be multiplied by the standard deviation of the specific covariate (both for continuous and categorical covariates).
`by.cov.type`	Split the plot for continuous and dichotomous covariates. Default is FALSE.
`abs.values`	Show the covariate coefficient in absolute values. Default is FALSE.
`show.data`	Show the actual covariate coefficients in the plot. Default is TRUE.
`show.means`	Show the means of included covariates (blue) and all covariates (grey) in the plot. Default is TRUE.
`show.bias`	Show estimated bias as text in the plot. Default is TRUE.
`dotpch`	The character used for plotting.
`labels`	Custom labels for the parameter-covariate relationships, (character vector)
`pch.mean`	The character used for plotting the mean.
`xlab`	Custom x-axis label
`ylab`	Custom y-axis label
`col`	The color scheme.
`...`	Additional plotting arguments may be passed to this function.

Details

Optionally, estimated bias is plotted in the graph (as text). Bias is also shown by the difference in mean of parameter estimates when the covariate is included (blue diamond), as opposed to the mean of all parameter estimates (grey diamond)

Note: For dichotomous covariates, the default PsN implementation is to use the most common covariate value as base, while the effect of the other value, is estimated by a theta. Xpose (bootscm.import) however recalculates the estimated parameters, to the parametrization in which the lowest value of the dichotomous covariate is the base (e.g. 0), and the estimated THETA denotes the proportional change, when the covariate has the other value (e.g. 1).

Value

No value returned.

Author(s)

Ron Keizer

Examples


  xp.boot.par.est()

xp.boot.par.est()

Correlations between covariate coefficients

Description

This function creates a plot showing the correlations in estimates for covariate coefficients, obtained from the first step (univariate testing) in each scm performed in the bootscm.

Usage

xp.boot.par.est.corr(
  bootgam.obj = NULL,
  sd.norm = TRUE,
  by.cov.type = FALSE,
  cov.plot = NULL,
  ask.covs = FALSE,
  dotpch = 19,
  col = rgb(0.2, 0.2, 0.9, 0.75),
  ...
)
xp.boot.par.est.corr(
  bootgam.obj = NULL,
  sd.norm = TRUE,
  by.cov.type = FALSE,
  cov.plot = NULL,
  ask.covs = FALSE,
  dotpch = 19,
  col = rgb(0.2, 0.2, 0.9, 0.75),
  ...
)

Arguments

`bootgam.obj`	The object created using bootscm.import(), which hold the data for plotting.
`sd.norm`	Perform normalization of the covariate coefficients (default is TRUE). When TRUE, the estimated covariate coefficients will be multiplied by the standard deviation of the specific covariate (both for continuous and categorical covariates).
`by.cov.type`	Split the plot for continuous and dichotomous covariates. Default is FALSE.
`cov.plot`	A character vector which lists the covariates to include in the plot. If none are specified (NULL), all covariate coefficients will be included in the plot.
`ask.covs`	Ask the user which covariates to include in the plot. Default is FALSE.
`dotpch`	The character used for plotting.
`col`	The colors used for plotting.
`...`	Additional plotting arguments may be passed to this function.

Value

No value returned.

Author(s)

Ron Keizer

Examples


## Not run: 
xp.boot.par.est.corr(current.bootscm, sd.norm = TRUE,
                          cov.plot = c("CLSEX", "VSEX", "CLWT"))


## End(Not run)
## Not run: 
xp.boot.par.est.corr(current.bootscm, sd.norm = TRUE,
                          cov.plot = c("CLSEX", "VSEX", "CLWT"))


## End(Not run)

Distribution of difference in AIC

Description

Distribution of difference in AIC

Usage

xp.daic.npar.plot(
  bootscm.obj = NULL,
  main = NULL,
  xlb = "Difference in AIC",
  ylb = "Density",
  ...
)
xp.daic.npar.plot(
  bootscm.obj = NULL,
  main = NULL,
  xlb = "Difference in AIC",
  ylb = "Density",
  ...
)

Arguments

`bootscm.obj`	a bootscm object.
`main`	The title of the plot
`xlb`	The x-label of the plot
`ylb`	The y-label of the plot
`...`	Additional parameters passed to `panel.xyplot` and `xyplot`.

Value

A lattice plot object.

Plot of model size distribution for a bootgam or bootscm

Description

This function creates a kernel smoothed plot of the number of covariates included in the final model in each gam/scm in the bootgam/bootscm procedure.

Usage

xp.distr.mod.size(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  bw = 0.5,
  xlb = NULL,
  ...
)
xp.distr.mod.size(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  bw = 0.5,
  xlb = NULL,
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	Plot title.
`bw`	The smoothing bandwidth to be used for the kernel.
`xlb`	The x-axis label.
`...`	Additional plotting parameter may be passed to this function.

Value

A lattice plot object will be returned.

Author(s)

Ron Keizer

Distribution of difference in OFV

Description

Distribution of difference in OFV

Usage

xp.dofv.npar.plot(
  bootscm.obj = NULL,
  main = NULL,
  xlb = "Difference in OFV",
  ylb = "Density",
  ...
)
xp.dofv.npar.plot(
  bootscm.obj = NULL,
  main = NULL,
  xlb = "Difference in OFV",
  ylb = "Density",
  ...
)

Arguments

`bootscm.obj`	a bootscm object.
`main`	The title of the plot
`xlb`	The x-label of the plot
`ylb`	The y-label of the plot
`...`	Additional parameters passed to `panel.xyplot` and `xyplot`.

Value

A lattice plot object.

OFV difference (optimism) plot.

Description

A plot of the difference in OFV between final bootscm models and the reference final scm model.

Usage

xp.dofv.plot(
  bootscm.obj = NULL,
  main = NULL,
  xlb = "Difference in OFV",
  ylb = "Density",
  ...
)
xp.dofv.plot(
  bootscm.obj = NULL,
  main = NULL,
  xlb = "Difference in OFV",
  ylb = "Density",
  ...
)

Arguments

`bootscm.obj`	The bootgam or bootscm object.
`main`	Plot title.
`xlb`	Label for x-axis.
`ylb`	Label for y-axis.
`...`	Additional plotting parameters.

Value

A lattice plot object is returned.

Author(s)

Ron Keizer

Default function for calculating dispersion in `xpose.gam`.

Description

Default function for calculating dispersion in xpose.gam.

Usage

xp.get.disp(gamdata, parnam, covnams, family = "gaussian", ...)
xp.get.disp(gamdata, parnam, covnams, family = "gaussian", ...)

Arguments

`gamdata`	the data used for a GAM
`parnam`	ONE (and only one) model parameter name.
`covnams`	Covariate names to test on parameter.
`family`	Assumption for the parameter distribution.
`...`	Used to pass arguments to more basic functions.

Value

a list including the dispersion

Trace plots for conditional indices

Description

Trace plots for conditional indices

Usage

xp.inc.cond.stab.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Bootstrap replicate number",
  ylb = "Conditional inclusion frequency",
  normalize = TRUE,
  split.plots = FALSE,
  ...
)
xp.inc.cond.stab.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Bootstrap replicate number",
  ylb = "Conditional inclusion frequency",
  normalize = TRUE,
  split.plots = FALSE,
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	The title of the plot
`xlb`	The x-label of the plot
`ylb`	The y-label of the plot
`normalize`	Should one normalize?
`split.plots`	Should the plots be split?
`...`	Additional parameters passed to `panel.xyplot` and `xyplot`.

Value

A lattice plot object.

Trace plots for conditional indices rper replicate number

Description

Trace plots for conditional indices rper replicate number

Usage

xp.inc.ind.cond.stab.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Bootstrap replicate number",
  ylb = "Conditional inclusion frequency",
  limits = c(0.2, 0.8),
  normalize = TRUE,
  split.plots = FALSE,
  start = 25,
  ...
)
xp.inc.ind.cond.stab.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Bootstrap replicate number",
  ylb = "Conditional inclusion frequency",
  limits = c(0.2, 0.8),
  normalize = TRUE,
  split.plots = FALSE,
  start = 25,
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	The title of the plot
`xlb`	The x-label of the plot
`ylb`	The y-label of the plot
`limits`	Limits for the inclusion index.
`normalize`	Should one normalize?
`split.plots`	Should the plots be split?
`start`	When to start.
`...`	Arguments passed to other functions.

Value

A lattice plot object.

Inclusion frequency plot

Description

Plot the inclusion frequencies of covariates in the final models obtained in a bootgam or bootscm. Covariates are ordered by inclusion frequency.

Usage

xp.inc.prob(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  col = "#6495ED",
  xlb = NULL,
  ylb = "Covariate",
  ...
)
xp.inc.prob(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  col = "#6495ED",
  xlb = NULL,
  ylb = "Covariate",
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	Plot title
`col`	Color used for the plot.
`xlb`	Label for x-axis.
`ylb`	Label for y-axis.
`...`	Additional plotting parameters.

Value

A lattice plot object will be returned.

Author(s)

Ron Keizer

Inclusion frequency plot for combination of covariates.

Description

Plot the inclusion frequency of the most common 2-covariate combinations.

Usage

xp.inc.prob.comb.2(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  col = "#6495ED",
  xlb = NULL,
  ylb = "Covariate combination",
  ...
)
xp.inc.prob.comb.2(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  col = "#6495ED",
  xlb = NULL,
  ylb = "Covariate combination",
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	Plot title
`col`	Color used for plot.
`xlb`	Label for x-axis.
`ylb`	Label for y-axis.
`...`	Additional plotting parameters.

Value

A lattice plot object will be returned.

Author(s)

Ron Keizer

Inclusion stability plot A plot of the inclusion frequency of covariates vs bootgam/bootscm iteration number. This plot can be used to evaluate whether sufficient iterations have been performed.

Description

Inclusion stability plot

A plot of the inclusion frequency of covariates vs bootgam/bootscm iteration number. This plot can be used to evaluate whether sufficient iterations have been performed.

Usage

xp.inc.stab.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  normalize = TRUE,
  split.plots = FALSE,
  xlb = "Bootstrap replicate number",
  ylb = "Difference of estimate with final",
  ...
)
xp.inc.stab.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  normalize = TRUE,
  split.plots = FALSE,
  xlb = "Bootstrap replicate number",
  ylb = "Difference of estimate with final",
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	Plot title
`normalize`	Should the plot be normalized?
`split.plots`	Should the plots be split?
`xlb`	The label for the x-axis.
`ylb`	The label for the y-axis.
`...`	Additional plotting parameters

Value

A lattice plot object is returned.

Author(s)

Ron Keizer

Plot of inclusion index of covariates.

Description

Covariate inclusion indices show the correlation in inclusion of a covariate in the final model in a bootgam or bootscm.

Usage

xp.incl.index.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Index",
  ylb = "Covariate",
  add.ci = FALSE,
  incl.range = NULL,
  return_plot = TRUE,
  results.tab = NULL,
  ...
)
xp.incl.index.cov(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Index",
  ylb = "Covariate",
  add.ci = FALSE,
  incl.range = NULL,
  return_plot = TRUE,
  results.tab = NULL,
  ...
)

Arguments

`bootgam.obj`	The bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	Plot title.
`xlb`	Label for the x-axis.
`ylb`	Label for the y-axis.
`add.ci`	Add a confidence interval to the plotted data.
`incl.range`	Included range
`return_plot`	Should the function return a plot?
`results.tab`	Specify your own results table.
`...`	Additional plotting information.

Value

A lattice plot object is returned.

Author(s)

Ron Keizer

Inclusion index individuals, compare between covariates.

Description

A plot showing the range of inclusion indices for individuals for all covariates. This plot can be used to evaluate whether there were covariates which were more influenced by the constituency of the bootstrapped dataset than others.

Usage

xp.incl.index.cov.comp(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Individual inclusion index",
  ylb = "ID",
  ...
)
xp.incl.index.cov.comp(
  bootgam.obj = NULL,
  boot.type = NULL,
  main = NULL,
  xlb = "Individual inclusion index",
  ylb = "ID",
  ...
)

Arguments

`bootgam.obj`	A bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`main`	The title of the plot.
`xlb`	The label for the x-axis.
`ylb`	The label for the y-axis.
`...`	Additional plotting parameters.

Value

A lattice plot object is returned.

Author(s)

Ron Keizer

Individual inclusion index

Description

This function will generate a plot of individual inclusion indexes for a specific covariate, which can be used to identify influential individuals for inclusion of that covariate. The index for an individual is calculated as the observed number of inclusions of that individual when the specific covariate was included minus the expected number of inclusions (based on the total bootstrap inclusions), divided by expected.

Usage

xp.incl.index.cov.ind(
  bootgam.obj = NULL,
  boot.type = NULL,
  cov.name = NULL,
  main = NULL,
  ylb = "ID",
  xlb = "Individual inclusion index",
  return_plot = TRUE,
  results.tab = NULL,
  ...
)
xp.incl.index.cov.ind(
  bootgam.obj = NULL,
  boot.type = NULL,
  cov.name = NULL,
  main = NULL,
  ylb = "ID",
  xlb = "Individual inclusion index",
  return_plot = TRUE,
  results.tab = NULL,
  ...
)

Arguments

`bootgam.obj`	A bootgam or bootscm object.
`boot.type`	Either "bootgam" or "bootscm". Default is NULL, which means the user will be asked to make a choice.
`cov.name`	The name of the covariate for which to create the plot.
`main`	The title of the plot.
`ylb`	The label for the x-axis.
`xlb`	The label for the y-axis.
`return_plot`	Should a plot object be returned?
`results.tab`	Supply your own results table.
`...`	Additional plotting parameters.

Value

A lattice plot object is returned.

Author(s)

Ron Keizer

Define a scope for the gam. Used as default input to the `scope` argument in `xpose.gam`

Description

Define a scope for the gam. Used as default input to the scope argument in xpose.gam

Usage

xp.scope3(
  object,
  covnam = xvardef("covariates", object),
  nmods = 3,
  smoother1 = 0,
  arg1 = NULL,
  smoother2 = 1,
  arg2 = NULL,
  smoother3 = "ns",
  arg3 = "df=2",
  smoother4 = "ns",
  arg4 = "df=3",
  excl1 = NULL,
  excl2 = NULL,
  excl3 = NULL,
  excl4 = NULL,
  extra = NULL,
  subset = xsubset(object),
  ...
)
xp.scope3(
  object,
  covnam = xvardef("covariates", object),
  nmods = 3,
  smoother1 = 0,
  arg1 = NULL,
  smoother2 = 1,
  arg2 = NULL,
  smoother3 = "ns",
  arg3 = "df=2",
  smoother4 = "ns",
  arg4 = "df=3",
  excl1 = NULL,
  excl2 = NULL,
  excl3 = NULL,
  excl4 = NULL,
  extra = NULL,
  subset = xsubset(object),
  ...
)

Arguments

`object`	An xpose.data object.
`covnam`	Covariate names to test.
`nmods`	Number of models to examine.
`smoother1`	Smoother for each model.
`arg1`	Argument for model 1.
`smoother2`	Smoother for each model.
`arg2`	Argument for model 2.
`smoother3`	Smoother for each model.
`arg3`	Argument for model 3.
`smoother4`	Smoother for each model.
`arg4`	Argument for model 4.
`excl1`	Covariate exclusion from model 1.
`excl2`	Covariate exclusion from model 2.
`excl3`	Covariate exclusion from model 3.
`excl4`	Covariate exclusion from model 4.
`extra`	Extra exclusion criteria.
`subset`	Subset on data.
`...`	Used to pass arguments to more basic functions.

Examples


xp.scope3(simpraz.xpdb)

xp.scope3(simpraz.xpdb)

Title

Description

Title

Usage

xpose.bootgam(
  object,
  n = n,
  id = object@Prefs@Xvardef$id,
  oid = "OID",
  seed = NULL,
  parnam = xvardef("parms", object)[1],
  covnams = xvardef("covariates", object),
  conv.value = object@[email protected]$conv.value,
  check.interval = as.numeric(object@[email protected]$check.interval),
  start.check = as.numeric(object@[email protected]$start.check),
  algo = object@[email protected]$algo,
  start.mod = object@[email protected]$start.mod,
  liif = as.numeric(object@[email protected]$liif),
  ljif.conv = as.numeric(object@[email protected]$ljif.conv),
  excluded.ids = as.numeric(object@[email protected]$excluded.ids),
  ...
)
xpose.bootgam(
  object,
  n = n,
  id = object@Prefs@Xvardef$id,
  oid = "OID",
  seed = NULL,
  parnam = xvardef("parms", object)[1],
  covnams = xvardef("covariates", object),
  conv.value = object@Prefs@Bootgam.prefs$conv.value,
  check.interval = as.numeric(object@Prefs@Bootgam.prefs$check.interval),
  start.check = as.numeric(object@Prefs@Bootgam.prefs$start.check),
  algo = object@Prefs@Bootgam.prefs$algo,
  start.mod = object@Prefs@Bootgam.prefs$start.mod,
  liif = as.numeric(object@Prefs@Bootgam.prefs$liif),
  ljif.conv = as.numeric(object@Prefs@Bootgam.prefs$ljif.conv),
  excluded.ids = as.numeric(object@Prefs@Bootgam.prefs$excluded.ids),
  ...
)

Arguments

`object`	An xpose.data object.
`n`	number of bootstrap iterations
`id`	column name of id
`oid`	create a new column with the original ID data
`seed`	random seed
`parnam`	ONE (and only one) model parameter name.
`covnams`	Covariate names to test on parameter.
`conv.value`	Convergence value
`check.interval`	How often to check the convergence
`start.check`	When to start checking
`algo`	Which algorithm to use
`start.mod`	which start model
`liif`	The liif value
`ljif.conv`	The convergence value for the liif
`excluded.ids`	ID values to exclude.
`...`	Used to pass arguments to more basic functions.

Value

a list of results from the bootstrap of the GAM.

Examples


## Not run: 
## filter out occasion as a covariate as only one value
all_covs <- xvardef("covariates",simpraz.xpdb)
some_covs <- all_covs[!(all_covs %in% "OCC") ] 

## here only running n=5 replicates to see that things work
##   use something like n=100 for resonable results
boot_gam_obj <- xpose.bootgam(simpraz.xpdb,5,parnam="KA",covnams=some_covs,seed=1234)

## End(Not run)

## Not run: 
## filter out occasion as a covariate as only one value
all_covs <- xvardef("covariates",simpraz.xpdb)
some_covs <- all_covs[!(all_covs %in% "OCC") ] 

## here only running n=5 replicates to see that things work
##   use something like n=100 for resonable results
boot_gam_obj <- xpose.bootgam(simpraz.xpdb,5,parnam="KA",covnams=some_covs,seed=1234)

## End(Not run)

Create an Xpose data object

Description

Creates an xpose.data object.

Usage

xpose.data(
  runno,
  tab.suffix = "",
  sim.suffix = "sim",
  cwres.suffix = "",
  directory = ".",
  quiet = TRUE,
  table.names = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab",
    "cwtab"),
  cwres.name = c("cwtab"),
  mod.prefix = "run",
  mod.suffix = ".mod",
  phi.suffix = ".phi",
  phi.file = NULL,
  nm7 = NULL,
  ...
)
xpose.data(
  runno,
  tab.suffix = "",
  sim.suffix = "sim",
  cwres.suffix = "",
  directory = ".",
  quiet = TRUE,
  table.names = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab",
    "cwtab"),
  cwres.name = c("cwtab"),
  mod.prefix = "run",
  mod.suffix = ".mod",
  phi.suffix = ".phi",
  phi.file = NULL,
  nm7 = NULL,
  ...
)

Arguments

`runno`	Run number of the table files to read.
`tab.suffix`	Suffix to be appended to the table file names for the "real" data.
`sim.suffix`	Suffix to be appended to the table file names for any simulated data.
`cwres.suffix`	Suffix to be appended to the table file names for any CWRES data.
`directory`	Where the files are located.
`quiet`	A logical value indicating if more diagnostic messages should be printed when running this function.
`table.names`	Default text that Xpose looks for when searching for table files.
`cwres.name`	default text that xpose looks for when searching for CWRES table files.
`mod.prefix`	Start of model file name.
`mod.suffix`	End of model file name.
`phi.suffix`	End of .phi file name.
`phi.file`	The name of the .phi file. If not `NULL` then supersedes `paste(mod.prefix,runno,phi.suffix,sep="")`.
`nm7`	`T/F` if table files are for NONMEM 7/6, NULL for undefined.
`...`	Extra arguments passed to function.

Details

Xpose expects, by default, to find at least one the the following NONMEM tables in the working directory to be able to create an Xpose data object (using a run number of '5' as an example):

sdtab5: The 'standard' parameters, including IWRE, IPRE, TIME, and the NONMEM default items (DV, PRED, RES and WRES) that are added when NOAPPEND is not present in the $TABLE record.

$TABLE ID TIME IPRE IWRE NOPRINT ONEHEADER FILE=sdtab5

patab5: The empirical Bayes estimates of individual model parameter values, or posthoc estimates. These are model parameters, such as CL, V2, ETA1, etc.

$TABLE ID CL V2 KA K F1 ETA1 ETA2 ETA3 NOPRINT NOAPPEND ONEHEADER FILE=patab5

catab5: Categorical covariates, e.g. SEX, RACE.

$TABLE ID SEX HIV GRP NOPRINT NOAPPEND ONEHEADER FILE=catab5

cotab5: Continuous covariates, e.g. WT, AGE.

$TABLE ID WT AGE BSA HT GGT HB NOPRINT NOAPPEND ONEHEADER FILE=cotab5

The default names for table files can be changed by changing the default values to the function. The files that Xpose looks for by default are:

paste(table.names, runno, tab.suffix, sep="")

The default CWRES table file name is called:

paste(cwres.name,runno,cwres.suffix,tab.suffix,sep="")

If there are simulation files present then Xpose looks for the files to be named:

paste(table.names, runno, sim.suffix, tab.suffix, sep="") paste(cwres.name,runno,sim.suffix,cwres.suffix,tab.suffix,sep="")

This is basically a wrapper function for the read.nm.tables, Data and SData functions. See them for further information.

Also reads in the .phi file associated with the run (Individual OFVs, parameters, and variances of those parameters.)

Value

An xpose.data object. Default values for this object are created from a file called 'xpose.ini'. This file can be found in the root directory of the 'xpose4' package:

system.file("xpose.ini",package="xpose4").

It can be modified to fit the users wants and placed in the home folder of the user or the working directory, to override default settings.

Author(s)

Niclas Jonsson, Andrew Hooker

Examples

# Here we create files from an example NONMEM run
 
od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

xpdb <- xpose.data(1)


file.remove(new.files) # remove these files
setwd(od)  # restore working directory


## Not run: 

# We expect to find the required NONMEM run and table files for run
# 5 in the current working directory, and that the table files have
# a suffix of '.dat', e.g. sdtab5.dat
xpdb5 <- xpose.data(5, tab.suffix = ".dat") 

## End(Not run)

# Here we create files from an example NONMEM run
 
od = setwd(tempdir()) # move to a temp directory
(cur.files <- dir()) # current files in temp directory

simprazExample(overwrite=TRUE) # write files
(new.files <- dir()[!(dir() %in% cur.files)])  # what files are new here?

xpdb <- xpose.data(1)


file.remove(new.files) # remove these files
setwd(od)  # restore working directory


## Not run: 

# We expect to find the required NONMEM run and table files for run
# 5 in the current working directory, and that the table files have
# a suffix of '.dat', e.g. sdtab5.dat
xpdb5 <- xpose.data(5, tab.suffix = ".dat") 

## End(Not run)

Class xpose.data

Description

The xpose.data class is the fundamental data object in Xpose 4. It contains the data and preferences used in the creation of the Xpose plots and analyses.

Objects from the Class

Objects are most easily created by the xpose.data function, which reads the appropriate NONMEM table files and populates the slots of the object.

Author(s)

Niclas Jonsson and Andrew Hooker

Stepwise GAM search for covariates on a parameter (Xpose 4)

Description

Function takes an Xpose object and performs a generalized additive model (GAM) stepwise search for influential covariates on a single model parameter.

Usage

xpose.gam(
  object,
  parnam = xvardef("parms", object)[1],
  covnams = xvardef("covariates", object),
  trace = TRUE,
  scope = NULL,
  disp = object@[email protected]$disp,
  start.mod = object@[email protected]$start.mod,
  family = "gaussian",
  wts.data = [email protected],
  wts.col = NULL,
  steppit = object@[email protected]$steppit,
  subset = xsubset(object),
  onlyfirst = object@[email protected]$onlyfirst,
  medianNorm = object@[email protected]$medianNorm,
  nmods = object@[email protected]$nmods,
  smoother1 = object@[email protected]$smoother1,
  smoother2 = object@[email protected]$smoother2,
  smoother3 = object@[email protected]$smoother3,
  smoother4 = object@[email protected]$smoother4,
  arg1 = object@[email protected]$arg1,
  arg2 = object@[email protected]$arg2,
  arg3 = object@[email protected]$arg3,
  arg4 = object@[email protected]$arg4,
  excl1 = object@[email protected]$excl1,
  excl2 = object@[email protected]$excl2,
  excl3 = object@[email protected]$excl3,
  excl4 = object@[email protected]$excl4,
  extra = object@[email protected]$extra,
  ...
)
xpose.gam(
  object,
  parnam = xvardef("parms", object)[1],
  covnams = xvardef("covariates", object),
  trace = TRUE,
  scope = NULL,
  disp = object@Prefs@Gam.prefs$disp,
  start.mod = object@Prefs@Gam.prefs$start.mod,
  family = "gaussian",
  wts.data = object@Data.firstonly,
  wts.col = NULL,
  steppit = object@Prefs@Gam.prefs$steppit,
  subset = xsubset(object),
  onlyfirst = object@Prefs@Gam.prefs$onlyfirst,
  medianNorm = object@Prefs@Gam.prefs$medianNorm,
  nmods = object@Prefs@Gam.prefs$nmods,
  smoother1 = object@Prefs@Gam.prefs$smoother1,
  smoother2 = object@Prefs@Gam.prefs$smoother2,
  smoother3 = object@Prefs@Gam.prefs$smoother3,
  smoother4 = object@Prefs@Gam.prefs$smoother4,
  arg1 = object@Prefs@Gam.prefs$arg1,
  arg2 = object@Prefs@Gam.prefs$arg2,
  arg3 = object@Prefs@Gam.prefs$arg3,
  arg4 = object@Prefs@Gam.prefs$arg4,
  excl1 = object@Prefs@Gam.prefs$excl1,
  excl2 = object@Prefs@Gam.prefs$excl2,
  excl3 = object@Prefs@Gam.prefs$excl3,
  excl4 = object@Prefs@Gam.prefs$excl4,
  extra = object@Prefs@Gam.prefs$extra,
  ...
)

Arguments

`object`	An xpose.data object.
`parnam`	ONE (and only one) model parameter name.
`covnams`	Covariate names to test on parameter.
`trace`	TRUE if you want GAM output to screen.
`scope`	Scope of the GAM search.
`disp`	If dispersion should be used in the GAM object.
`start.mod`	Starting model.
`family`	Assumption for the parameter distribution.
`wts.data`	Weights on the least squares fitting of parameter vs. covariate. Often one can use the variances of the individual parameter values as weights. This data frame must have column with name ID and any subset variable as well as the variable defined by the `wts.col`.
`wts.col`	Which column in the `wts.data` to use.
`steppit`	TRUE for stepwise search, false for no search.
`subset`	Subset on data.
`onlyfirst`	TRUE if only the first row of each individual's data is to be used.
`medianNorm`	Normalize to the median of parameter and covariates.
`nmods`	Number of models to examine.
`smoother1`	Smoother for each model.
`smoother2`	Smoother for each model.
`smoother3`	Smoother for each model.
`smoother4`	Smoother for each model.
`arg1`	Argument for model 1.
`arg2`	Argument for model 2.
`arg3`	Argument for model 3.
`arg4`	Argument for model 4.
`excl1`	Covariate exclusion from model 1.
`excl2`	Covariate exclusion from model 2.
`excl3`	Covariate exclusion from model 3.
`excl4`	Covariate exclusion from model 4.
`extra`	Extra exclusion criteria.
`...`	Used to pass arguments to more basic functions.

Value

Returned is a step.Gam object. In this object the step-wise-selected model is returned, with up to two additional components. There is an "anova" component corresponding to the steps taken in the search, as well as a "keep" component if the "keep=" argument was supplied in the call.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples

## Run a GAM using the example xpose database 
gam_ka <- xpose.gam(simpraz.xpdb, parnam="KA")

## Summarize GAM
xp.summary(gam_ka)

## GAM residuals of base model vs. covariates
xp.plot(gam_ka)

## An Akaike plot of the results
xp.akaike.plot(gam_ka)

## Studentized residuals
xp.ind.stud.res(gam_ka)

## Individual influence on GAM fit
xp.ind.inf.fit(gam_ka)

## Individual influence on GAM terms
xp.ind.inf.terms(gam_ka)

## Individual parameters to GAM fit
xp.cook(gam_ka)

## Run a GAM using the example xpose database 
gam_ka <- xpose.gam(simpraz.xpdb, parnam="KA")

## Summarize GAM
xp.summary(gam_ka)

## GAM residuals of base model vs. covariates
xp.plot(gam_ka)

## An Akaike plot of the results
xp.akaike.plot(gam_ka)

## Studentized residuals
xp.ind.stud.res(gam_ka)

## Individual influence on GAM fit
xp.ind.inf.fit(gam_ka)

## Individual influence on GAM terms
xp.ind.inf.terms(gam_ka)

## Individual parameters to GAM fit
xp.cook(gam_ka)

Displays the Xpose license and citation information

Description

This function displays a copy of Xpose's end user license agreement (EULA).

Usage

xpose.license.citation()
xpose.license.citation()

Value

The EULA.

Author(s)

Andrew Hooker

Examples


xpose.license.citation()

xpose.license.citation()

Functions to create nice looking axes when using Log scales.

Description

The functions are used to create standard tic marks and axis labels when the axes are on the log scale.

Usage

xpose.logTicks(lim, loc = c(1, 5))

xpose.yscale.components.log10(lim, ...)

xpose.xscale.components.log10(lim, ...)
xpose.logTicks(lim, loc = c(1, 5))

xpose.yscale.components.log10(lim, ...)

xpose.xscale.components.log10(lim, ...)

Arguments

`lim`	Limits
`loc`	Locations
`...`	Additional arguments passed to the function.

Details

These functions create log scales that look like they should (not the default R scales). These functions are used as input to the xscale.components argument in a lattice plot.

Functions

xpose.logTicks(): Make log tic marks
xpose.xscale.components.log10(): Make log scale on x-axis

Author(s)

Andrew Hooker

Examples


## Not run: 
xpdb5 <- xpose.data(5)
xpose.plot.default("PRED","DV",xpdb,logy=T,logx=T)
xpose.plot.default("PRED","DV",xpdb,logy=T,logx=T,
                   yscale.components = xpose.yscale.components.log10,
                   xscale.components = xpose.xscale.components.log10)

## both give the same result

## End(Not run)

## Not run: 
xpdb5 <- xpose.data(5)
xpose.plot.default("PRED","DV",xpdb,logy=T,logx=T)
xpose.plot.default("PRED","DV",xpdb,logy=T,logx=T,
                   yscale.components = xpose.yscale.components.log10,
                   xscale.components = xpose.xscale.components.log10)

## both give the same result

## End(Not run)

Create and object with class "xpose.multiple.plot".

Description

Create and object with class "xpose.multiple.plot".

Usage

xpose.multiple.plot(
  plotList,
  plotTitle = NULL,
  nm7 = TRUE,
  prompt = FALSE,
  new.first.window = FALSE,
  max.plots.per.page = 4,
  title = list(title.x = unit(0.5, "npc"), title.y = unit(0.5, "npc"), title.gp =
    gpar(cex = 1.2, fontface = "bold"), title.just = c("center", "center")),
  mirror = FALSE,
  bql.layout = FALSE,
  ...
)
xpose.multiple.plot(
  plotList,
  plotTitle = NULL,
  nm7 = TRUE,
  prompt = FALSE,
  new.first.window = FALSE,
  max.plots.per.page = 4,
  title = list(title.x = unit(0.5, "npc"), title.y = unit(0.5, "npc"), title.gp =
    gpar(cex = 1.2, fontface = "bold"), title.just = c("center", "center")),
  mirror = FALSE,
  bql.layout = FALSE,
  ...
)

Arguments

`plotList`	A list of lattice plots.
`plotTitle`	Main title for plots.
`nm7`	`TRUE` if we are using NONMEM 7
`prompt`	When printing should we prompt for each new page in plot?
`new.first.window`	`TRUE` or `FALSE`.
`max.plots.per.page`	A number. Max value is 9.
`title`	Title properties.
`mirror`	Are there mirror plots in plot list?
`bql.layout`	Should we use layout optimized for plots with BQL (below limit of quantification) measurements?
`...`	Additional options passed to function.

Value

An object of class "xpose.multiple.plot".

Author(s)

Niclas Jonsson and Andrew C. Hooker

Class for creating multiple plots in xpose

Description

Class for creating multiple plots in xpose

Slots

plotList: A list of lattice plots
plotTitle: The plot title
prompt: Should prompts be used
new.first.window: Create a new first window?
max.plots.per.page: How many plots per page?
title: The title
mirror: Are there mirror plots to create
bql.layout: Should we use bql.layout

Xpose 4 generic function for plotting multiple lattice objects on one page

Description

Function takes a list of lattice plot objects and prints them in a multiple plot layout with a title.

Usage

xpose.multiple.plot.default(
  plotList,
  plotTitle = NULL,
  prompt = FALSE,
  new.first.window = FALSE,
  max.plots.per.page = 4,
  title = list(title.x = unit(0.5, "npc"), title.y = unit(0.5, "npc"), title.gp =
    gpar(cex = 1.2, fontface = "bold"), title.just = c("center", "center")),
  mirror = FALSE,
  bql.layout = FALSE,
  page.numbers = TRUE,
  ...
)
xpose.multiple.plot.default(
  plotList,
  plotTitle = NULL,
  prompt = FALSE,
  new.first.window = FALSE,
  max.plots.per.page = 4,
  title = list(title.x = unit(0.5, "npc"), title.y = unit(0.5, "npc"), title.gp =
    gpar(cex = 1.2, fontface = "bold"), title.just = c("center", "center")),
  mirror = FALSE,
  bql.layout = FALSE,
  page.numbers = TRUE,
  ...
)

Arguments

`plotList`	A list of lattice plot objects such that plot object i can be called with `plotList[[i]]`
`plotTitle`	The title used for the multiple plot layout
`prompt`	If more than one page is needed do you want a prompt at the command line before the next page is printed
`new.first.window`	Should the first page of this plot be in the already opened window or should a new window be created
`max.plots.per.page`	Maximum number of plots per page in the multiple layout
`title`	Look of title using grid.
`mirror`	if the list contains mirror plots
`bql.layout`	should we use layout optimized for BQL measurements?
`page.numbers`	Should we add page numbers to multiple page plots?
`...`	Other arguments passed to the code in this function

Details

Additional arguments:

title.x: Where the title should be placed in the title grid region
title.y: Where the title should be placed in the title grid region
title.just: how the title should be justified
title.gp: The par parameters for the title (see grid)

Value

returns nothing

Author(s)

Andrew Hooker

Default box-and-whisker panel function for Xpose 4

Description

This is the box-and-whisker panel function for Xpose 4. This is not intended to be used outside the xpose.plot.bw function. Most of the arguments take their default values from xpose.data object but this can be overridden by supplying them as arguments to xpose.plot.bw.

Usage

xpose.panel.bw(
  x,
  y,
  object,
  subscripts,
  groups = NULL,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  xvarnam = NULL,
  yvarnam = NULL,
  type = object@[email protected]$type,
  col = object@[email protected]$col,
  pch = object@[email protected]$pch,
  cex = object@[email protected]$cex,
  lty = object@[email protected]$lty,
  fill = object@[email protected]$col,
  ids = NULL,
  idsmode = object@[email protected]$idsmode,
  idsext = object@[email protected]$idsext,
  idscex = object@[email protected]$idscex,
  idsdir = object@[email protected]$idsdir,
  bwhoriz = object@[email protected]$bwhoriz,
  bwratio = object@[email protected]$bwratio,
  bwvarwid = object@[email protected]$bwvarwid,
  bwdotpch = object@[email protected]$bwdotpch,
  bwdotcol = object@[email protected]$bwdotcol,
  bwdotcex = object@[email protected]$bwdotcex,
  bwreccol = object@[email protected]$bwreccol,
  bwrecfill = object@[email protected]$bwrecfill,
  bwreclty = object@[email protected]$bwreclty,
  bwreclwd = object@[email protected]$bwreclwd,
  bwumbcol = object@[email protected]$bwumbcol,
  bwumblty = object@[email protected]$bwumblty,
  bwumblwd = object@[email protected]$bwumblwd,
  bwoutcol = object@[email protected]$bwoutcol,
  bwoutcex = object@[email protected]$bwoutcex,
  bwoutpch = object@[email protected]$bwoutpch,
  grid = object@[email protected]$grid,
  logy = FALSE,
  logx = FALSE,
  force.x.continuous = TRUE,
  binvar = NULL,
  bins = 10,
  ...
)
xpose.panel.bw(
  x,
  y,
  object,
  subscripts,
  groups = NULL,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  xvarnam = NULL,
  yvarnam = NULL,
  type = object@Prefs@Graph.prefs$type,
  col = object@Prefs@Graph.prefs$col,
  pch = object@Prefs@Graph.prefs$pch,
  cex = object@Prefs@Graph.prefs$cex,
  lty = object@Prefs@Graph.prefs$lty,
  fill = object@Prefs@Graph.prefs$col,
  ids = NULL,
  idsmode = object@Prefs@Graph.prefs$idsmode,
  idsext = object@Prefs@Graph.prefs$idsext,
  idscex = object@Prefs@Graph.prefs$idscex,
  idsdir = object@Prefs@Graph.prefs$idsdir,
  bwhoriz = object@Prefs@Graph.prefs$bwhoriz,
  bwratio = object@Prefs@Graph.prefs$bwratio,
  bwvarwid = object@Prefs@Graph.prefs$bwvarwid,
  bwdotpch = object@Prefs@Graph.prefs$bwdotpch,
  bwdotcol = object@Prefs@Graph.prefs$bwdotcol,
  bwdotcex = object@Prefs@Graph.prefs$bwdotcex,
  bwreccol = object@Prefs@Graph.prefs$bwreccol,
  bwrecfill = object@Prefs@Graph.prefs$bwrecfill,
  bwreclty = object@Prefs@Graph.prefs$bwreclty,
  bwreclwd = object@Prefs@Graph.prefs$bwreclwd,
  bwumbcol = object@Prefs@Graph.prefs$bwumbcol,
  bwumblty = object@Prefs@Graph.prefs$bwumblty,
  bwumblwd = object@Prefs@Graph.prefs$bwumblwd,
  bwoutcol = object@Prefs@Graph.prefs$bwoutcol,
  bwoutcex = object@Prefs@Graph.prefs$bwoutcex,
  bwoutpch = object@Prefs@Graph.prefs$bwoutpch,
  grid = object@Prefs@Graph.prefs$grid,
  logy = FALSE,
  logx = FALSE,
  force.x.continuous = TRUE,
  binvar = NULL,
  bins = 10,
  ...
)

Arguments

`x`	Name(s) of the x-variable.
`y`	Name(s) of the y-variable.
`object`	An xpose.data object.
`subscripts`	The standard Trellis subscripts argument (see `xyplot`).
`groups`	Name of the variable used for superpose plots.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`xvarnam`	Character string with the name of the x-variable.
`yvarnam`	Character string with the name of the y-variable.
`type`	Character value indicating the type of display to use: "l"=lines, "p"=points, "b"=both points and lines.
`col`	Colour of lines and plot symbols.
`pch`	Plot character to use.
`cex`	Size of the plot characters.
`lty`	Line type.
`fill`	Fill colour.
`ids`	Character value with the name of the variable to label data points with.
`idsmode`	Determines the way text labels are added to plots. `NULL` means that only extreme points are labelled. Non-`NULL` means all data points are labelled. (See `link{xpose.plot.default}`)
`idsext`	See `link{xpose.plot.bw}`
`idscex`	Size of text labels.
`idsdir`	A value of "both" (the default) means that both high and low extreme points are labelled while "up" and "down" labels the high and low extreme points respectively. See `xpose.plot.bw`
`bwhoriz`	logical value indicating whether box and whiskers should be horizontal or not. The default is FALSE.
`bwratio`	Ratio of box height to inter-box space. The default is 1.5. An argument for `panel.bwplot`.
`bwvarwid`	Logical. If TRUE, widths of boxplots are proportional to the number of points used in creating it. The default is FALSE. An argument for `panel.bwplot`.
`bwdotpch`	Graphical parameter controlling the dot plotting character 'bwdotpch="\|"' is treated specially, by replacing the dot with a line. The default is 16. An argument for `panel.bwplot`.
`bwdotcol`	Graphical parameter controlling the dot colour - an integer or string. See 'col'. The default is black. An argument for `panel.bwplot`.
`bwdotcex`	The amount by which plotting text and symbols should be scaled relative to the default. 'NULL' and 'NA' are equivalent to '1.0'. An argument for `panel.bwplot`.
`bwreccol`	The colour to use for the box rectangle - an integer or string. The default is blue. See `trellis.par.get` and "box.rectangle".
`bwrecfill`	The colour to use for filling the box rectangle - an integer or string. The default is transparent (none). See `trellis.par.get` and "box.rectangle".
`bwreclty`	The line type for the box rectangle - an integer or string. The default is solid. See `trellis.par.get` and "box.rectangle".
`bwreclwd`	The width of the lines for the box rectangle - an integer. The default is 1. See `trellis.par.get` and "box.rectangle".
`bwumbcol`	The colour to use for the umbrellas - an integer or string. The default is blue. See `trellis.par.get` and "box.umbrella".
`bwumblty`	The line type for the umbrellas - an integer or string. The default is solid.See `trellis.par.get` and "box.umbrella".
`bwumblwd`	the width of the lines for the umbrellas - an integer. The default is 1. See `trellis.par.get` and "box.umbrella".
`bwoutcol`	The colour to use for the outliers - an integer or string. The default is blue. See `trellis.par.get` and "box.symbol".
`bwoutcex`	The amount by which outlier points should be scaled relative to the default. 'NULL' and 'NA' are equivalent to '1.0'. The default is 0.8. See `trellis.par.get` and "box.symbol".
`bwoutpch`	The plotting character, or symbol, to use for outlier points. Specified as an integer. See R help on 'points'. The default is an open circle. See `trellis.par.get` and "box.symbol".
`grid`	logical value indicating whether a visual reference grid should be added to the graph. (Could use arguments for line type, color etc).
`logy`	Logical value indicating whether the y-axis should be logarithmic.
`logx`	Logical value indicating whether the x-axis should be logarithmic.
`force.x.continuous`	Logical value indicating whether x-values should be taken as continuous, even if categorical.
`binvar`	Variable to be used for binning.
`bins`	The number of bins to be used. The default is 10.
`...`	Other arguments that may be needed in the function.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Default panel function for Xpose 4

Description

This is the panel function for Xpose 4. This is not intended to be ised outside the xpose.plot.default function. Most of the arguments take their default values from xpose.data object but this can be overridden by supplying them as argument to xpose.plot.default.

Usage

xpose.panel.default(
  x,
  y,
  object,
  subscripts,
  groups = object@Prefs@Xvardef$id,
  grp.col = NULL,
  iplot = NULL,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  xvarnam = NULL,
  yvarnam = NULL,
  PI = NULL,
  PI.subset = NULL,
  PI.bin.table = NULL,
  PI.real = NULL,
  PI.mirror = NULL,
  PI.ci = NULL,
  PPI = NULL,
  PI.mean = FALSE,
  PI.delta.mean = FALSE,
  PI.x.median = TRUE,
  PI.rug = "Default",
  PI.rug.col = "orange",
  PI.rug.lwd = 3,
  PI.identify.outliers = TRUE,
  PI.outliers.col = "red",
  PI.outliers.pch = 8,
  PI.outliers.cex = 1,
  PI.limits = c(0.025, 0.975),
  PI.arcol = "lightgreen",
  PI.up.lty = 2,
  PI.up.type = "l",
  PI.up.col = "black",
  PI.up.lwd = 2,
  PI.down.lty = 2,
  PI.down.type = "l",
  PI.down.col = "black",
  PI.down.lwd = 2,
  PI.med.lty = 1,
  PI.med.type = "l",
  PI.med.col = "black",
  PI.med.lwd = 2,
  PI.mean.lty = 3,
  PI.mean.type = "l",
  PI.mean.col = "black",
  PI.mean.lwd = 2,
  PI.delta.mean.lty = 3,
  PI.delta.mean.type = "l",
  PI.delta.mean.col = "black",
  PI.delta.mean.lwd = 2,
  PI.real.up.lty = 2,
  PI.real.up.type = "l",
  PI.real.up.col = "red",
  PI.real.up.lwd = 2,
  PI.real.down.lty = 2,
  PI.real.down.type = "l",
  PI.real.down.col = "red",
  PI.real.down.lwd = 2,
  PI.real.med.lty = 1,
  PI.real.med.type = "l",
  PI.real.med.col = "red",
  PI.real.med.lwd = 2,
  PI.real.mean.lty = 3,
  PI.real.mean.type = "l",
  PI.real.mean.col = "red",
  PI.real.mean.lwd = 2,
  PI.real.delta.mean.lty = 3,
  PI.real.delta.mean.type = "l",
  PI.real.delta.mean.col = "red",
  PI.real.delta.mean.lwd = 2,
  PI.mirror.up.lty = 2,
  PI.mirror.up.type = "l",
  PI.mirror.up.col = "darkgreen",
  PI.mirror.up.lwd = 1,
  PI.mirror.down.lty = 2,
  PI.mirror.down.type = "l",
  PI.mirror.down.col = "darkgreen",
  PI.mirror.down.lwd = 1,
  PI.mirror.med.lty = 1,
  PI.mirror.med.type = "l",
  PI.mirror.med.col = "darkgreen",
  PI.mirror.med.lwd = 1,
  PI.mirror.mean.lty = 3,
  PI.mirror.mean.type = "l",
  PI.mirror.mean.col = "darkgreen",
  PI.mirror.mean.lwd = 1,
  PI.mirror.delta.mean.lty = 3,
  PI.mirror.delta.mean.type = "l",
  PI.mirror.delta.mean.col = "darkgreen",
  PI.mirror.delta.mean.lwd = 1,
  PI.ci.up.arcol = "blue",
  PI.ci.up.lty = 3,
  PI.ci.up.type = "l",
  PI.ci.up.col = "darkorange",
  PI.ci.up.lwd = 2,
  PI.ci.down.arcol = "blue",
  PI.ci.down.lty = 3,
  PI.ci.down.type = "l",
  PI.ci.down.col = "darkorange",
  PI.ci.down.lwd = 2,
  PI.ci.med.arcol = "red",
  PI.ci.med.lty = 4,
  PI.ci.med.type = "l",
  PI.ci.med.col = "darkorange",
  PI.ci.med.lwd = 2,
  PI.ci.mean.arcol = "purple",
  PI.ci.mean.lty = 4,
  PI.ci.mean.type = "l",
  PI.ci.mean.col = "darkorange",
  PI.ci.mean.lwd = 2,
  PI.ci.delta.mean.arcol = "purple",
  PI.ci.delta.mean.lty = 4,
  PI.ci.delta.mean.type = "l",
  PI.ci.delta.mean.col = "darkorange",
  PI.ci.delta.mean.lwd = 2,
  PI.ci.area.smooth = FALSE,
  type = object@[email protected]$type,
  col = object@[email protected]$col,
  pch = object@[email protected]$pch,
  cex = object@[email protected]$cex,
  lty = object@[email protected]$lty,
  lwd = object@[email protected]$lwd,
  fill = object@[email protected]$fill,
  ids = NULL,
  idsmode = object@[email protected]$idsmode,
  idsext = object@[email protected]$idsext,
  idscex = object@[email protected]$idscex,
  idsdir = object@[email protected]$idsdir,
  abline = object@[email protected]$abline,
  abllwd = object@[email protected]$abllwd,
  abllty = object@[email protected]$abllty,
  ablcol = object@[email protected]$ablcol,
  smooth = object@[email protected]$smooth,
  smlwd = object@[email protected]$smlwd,
  smlty = object@[email protected]$smlty,
  smcol = object@[email protected]$smcol,
  smspan = object@[email protected]$smspan,
  smdegr = object@[email protected]$smdegr,
  smooth.for.groups = NULL,
  lmline = object@[email protected]$lmline,
  lmlwd = object@[email protected]$lmlwd,
  lmlty = object@[email protected]$lmlty,
  lmcol = object@[email protected]$lmcol,
  suline = object@[email protected]$suline,
  sulwd = object@[email protected]$sulwd,
  sulty = object@[email protected]$sulty,
  sucol = object@[email protected]$sucol,
  suspan = object@[email protected]$suspan,
  sudegr = object@[email protected]$sudegr,
  grid = object@[email protected]$grid,
  logy = FALSE,
  logx = FALSE,
  force.x.continuous = FALSE,
  bwhoriz = object@[email protected]$bwhoriz,
  bwratio = object@[email protected]$bwratio,
  bwvarwid = object@[email protected]$bwvarwid,
  bwdotpch = object@[email protected]$bwdotpch,
  bwdotcol = object@[email protected]$bwdotcol,
  bwdotcex = object@[email protected]$bwdotcex,
  bwreccol = object@[email protected]$bwreccol,
  bwrecfill = object@[email protected]$bwrecfill,
  bwreclty = object@[email protected]$bwreclty,
  bwreclwd = object@[email protected]$bwreclwd,
  bwumbcol = object@[email protected]$bwumbcol,
  bwumblty = object@[email protected]$bwumblty,
  bwumblwd = object@[email protected]$bwumblwd,
  bwoutcol = object@[email protected]$bwoutcol,
  bwoutcex = object@[email protected]$bwoutcex,
  bwoutpch = object@[email protected]$bwoutpch,
  autocorr = FALSE,
  vline = NULL,
  vllwd = 3,
  vllty = 2,
  vlcol = "grey",
  hline = NULL,
  hllwd = 3,
  hllty = 1,
  hlcol = "grey",
  pch.ip.sp = pch,
  cex.ip.sp = cex,
  ...
)
xpose.panel.default(
  x,
  y,
  object,
  subscripts,
  groups = object@Prefs@Xvardef$id,
  grp.col = NULL,
  iplot = NULL,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  xvarnam = NULL,
  yvarnam = NULL,
  PI = NULL,
  PI.subset = NULL,
  PI.bin.table = NULL,
  PI.real = NULL,
  PI.mirror = NULL,
  PI.ci = NULL,
  PPI = NULL,
  PI.mean = FALSE,
  PI.delta.mean = FALSE,
  PI.x.median = TRUE,
  PI.rug = "Default",
  PI.rug.col = "orange",
  PI.rug.lwd = 3,
  PI.identify.outliers = TRUE,
  PI.outliers.col = "red",
  PI.outliers.pch = 8,
  PI.outliers.cex = 1,
  PI.limits = c(0.025, 0.975),
  PI.arcol = "lightgreen",
  PI.up.lty = 2,
  PI.up.type = "l",
  PI.up.col = "black",
  PI.up.lwd = 2,
  PI.down.lty = 2,
  PI.down.type = "l",
  PI.down.col = "black",
  PI.down.lwd = 2,
  PI.med.lty = 1,
  PI.med.type = "l",
  PI.med.col = "black",
  PI.med.lwd = 2,
  PI.mean.lty = 3,
  PI.mean.type = "l",
  PI.mean.col = "black",
  PI.mean.lwd = 2,
  PI.delta.mean.lty = 3,
  PI.delta.mean.type = "l",
  PI.delta.mean.col = "black",
  PI.delta.mean.lwd = 2,
  PI.real.up.lty = 2,
  PI.real.up.type = "l",
  PI.real.up.col = "red",
  PI.real.up.lwd = 2,
  PI.real.down.lty = 2,
  PI.real.down.type = "l",
  PI.real.down.col = "red",
  PI.real.down.lwd = 2,
  PI.real.med.lty = 1,
  PI.real.med.type = "l",
  PI.real.med.col = "red",
  PI.real.med.lwd = 2,
  PI.real.mean.lty = 3,
  PI.real.mean.type = "l",
  PI.real.mean.col = "red",
  PI.real.mean.lwd = 2,
  PI.real.delta.mean.lty = 3,
  PI.real.delta.mean.type = "l",
  PI.real.delta.mean.col = "red",
  PI.real.delta.mean.lwd = 2,
  PI.mirror.up.lty = 2,
  PI.mirror.up.type = "l",
  PI.mirror.up.col = "darkgreen",
  PI.mirror.up.lwd = 1,
  PI.mirror.down.lty = 2,
  PI.mirror.down.type = "l",
  PI.mirror.down.col = "darkgreen",
  PI.mirror.down.lwd = 1,
  PI.mirror.med.lty = 1,
  PI.mirror.med.type = "l",
  PI.mirror.med.col = "darkgreen",
  PI.mirror.med.lwd = 1,
  PI.mirror.mean.lty = 3,
  PI.mirror.mean.type = "l",
  PI.mirror.mean.col = "darkgreen",
  PI.mirror.mean.lwd = 1,
  PI.mirror.delta.mean.lty = 3,
  PI.mirror.delta.mean.type = "l",
  PI.mirror.delta.mean.col = "darkgreen",
  PI.mirror.delta.mean.lwd = 1,
  PI.ci.up.arcol = "blue",
  PI.ci.up.lty = 3,
  PI.ci.up.type = "l",
  PI.ci.up.col = "darkorange",
  PI.ci.up.lwd = 2,
  PI.ci.down.arcol = "blue",
  PI.ci.down.lty = 3,
  PI.ci.down.type = "l",
  PI.ci.down.col = "darkorange",
  PI.ci.down.lwd = 2,
  PI.ci.med.arcol = "red",
  PI.ci.med.lty = 4,
  PI.ci.med.type = "l",
  PI.ci.med.col = "darkorange",
  PI.ci.med.lwd = 2,
  PI.ci.mean.arcol = "purple",
  PI.ci.mean.lty = 4,
  PI.ci.mean.type = "l",
  PI.ci.mean.col = "darkorange",
  PI.ci.mean.lwd = 2,
  PI.ci.delta.mean.arcol = "purple",
  PI.ci.delta.mean.lty = 4,
  PI.ci.delta.mean.type = "l",
  PI.ci.delta.mean.col = "darkorange",
  PI.ci.delta.mean.lwd = 2,
  PI.ci.area.smooth = FALSE,
  type = object@Prefs@Graph.prefs$type,
  col = object@Prefs@Graph.prefs$col,
  pch = object@Prefs@Graph.prefs$pch,
  cex = object@Prefs@Graph.prefs$cex,
  lty = object@Prefs@Graph.prefs$lty,
  lwd = object@Prefs@Graph.prefs$lwd,
  fill = object@Prefs@Graph.prefs$fill,
  ids = NULL,
  idsmode = object@Prefs@Graph.prefs$idsmode,
  idsext = object@Prefs@Graph.prefs$idsext,
  idscex = object@Prefs@Graph.prefs$idscex,
  idsdir = object@Prefs@Graph.prefs$idsdir,
  abline = object@Prefs@Graph.prefs$abline,
  abllwd = object@Prefs@Graph.prefs$abllwd,
  abllty = object@Prefs@Graph.prefs$abllty,
  ablcol = object@Prefs@Graph.prefs$ablcol,
  smooth = object@Prefs@Graph.prefs$smooth,
  smlwd = object@Prefs@Graph.prefs$smlwd,
  smlty = object@Prefs@Graph.prefs$smlty,
  smcol = object@Prefs@Graph.prefs$smcol,
  smspan = object@Prefs@Graph.prefs$smspan,
  smdegr = object@Prefs@Graph.prefs$smdegr,
  smooth.for.groups = NULL,
  lmline = object@Prefs@Graph.prefs$lmline,
  lmlwd = object@Prefs@Graph.prefs$lmlwd,
  lmlty = object@Prefs@Graph.prefs$lmlty,
  lmcol = object@Prefs@Graph.prefs$lmcol,
  suline = object@Prefs@Graph.prefs$suline,
  sulwd = object@Prefs@Graph.prefs$sulwd,
  sulty = object@Prefs@Graph.prefs$sulty,
  sucol = object@Prefs@Graph.prefs$sucol,
  suspan = object@Prefs@Graph.prefs$suspan,
  sudegr = object@Prefs@Graph.prefs$sudegr,
  grid = object@Prefs@Graph.prefs$grid,
  logy = FALSE,
  logx = FALSE,
  force.x.continuous = FALSE,
  bwhoriz = object@Prefs@Graph.prefs$bwhoriz,
  bwratio = object@Prefs@Graph.prefs$bwratio,
  bwvarwid = object@Prefs@Graph.prefs$bwvarwid,
  bwdotpch = object@Prefs@Graph.prefs$bwdotpch,
  bwdotcol = object@Prefs@Graph.prefs$bwdotcol,
  bwdotcex = object@Prefs@Graph.prefs$bwdotcex,
  bwreccol = object@Prefs@Graph.prefs$bwreccol,
  bwrecfill = object@Prefs@Graph.prefs$bwrecfill,
  bwreclty = object@Prefs@Graph.prefs$bwreclty,
  bwreclwd = object@Prefs@Graph.prefs$bwreclwd,
  bwumbcol = object@Prefs@Graph.prefs$bwumbcol,
  bwumblty = object@Prefs@Graph.prefs$bwumblty,
  bwumblwd = object@Prefs@Graph.prefs$bwumblwd,
  bwoutcol = object@Prefs@Graph.prefs$bwoutcol,
  bwoutcex = object@Prefs@Graph.prefs$bwoutcex,
  bwoutpch = object@Prefs@Graph.prefs$bwoutpch,
  autocorr = FALSE,
  vline = NULL,
  vllwd = 3,
  vllty = 2,
  vlcol = "grey",
  hline = NULL,
  hllwd = 3,
  hllty = 1,
  hlcol = "grey",
  pch.ip.sp = pch,
  cex.ip.sp = cex,
  ...
)

Arguments

`x`	Name(s) of the x-variable.
`y`	Name(s) of the y-variable.
`object`	An xpose.data object.
`subscripts`	The standard Trellis subscripts argument (see `xyplot`)
`groups`	Name of the variable used for superpose plots.
`grp.col`	Logical value indicating whether or not to use colour highlighting when groups are specified. NULL means no highlighting, while TRUE will identify group members by colour.
`iplot`	Is this an individual plots matrix? Internal use only.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot.
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`xvarnam`	Character string with the name of the x-variable.
`yvarnam`	Character string with the name of the y-variable.
`PI`	Either "lines", "area" or "both" specifying whether prediction intervals (as lines, as a shaded area or both) should be computed from the data in `SData` and added to the display. `NULL` means no prediction interval.
`PI.subset`	The subset to be used for the PI.
`PI.bin.table`	The table used to create VPC plots. Has a specific format created by `read.npc.vpc.results`
`PI.real`	Plot the percentiles of the real data in the various bins. values can be NULL or TRUE. Note that for a bin with few actual observations the percentiles will be approximate. For example, the 95th percentile of 4 data points will always be the largest of the 4 data points.
`PI.mirror`	Plot the percentiles of one simulated data set in each bin. values allowed are `NULL`, `TRUE` or `AN.INTEGER.VALUE`. `TRUE` takes the first mirror from `PI.bin.table` and `AN.INTEGER.VALUE` can be `1, 2, ...{} n` where `n` is the number of mirror's output in the `PI.bin.table`. Used mainly by `xpose.VPC`.
`PI.ci`	Plot the prediction interval of the simulated data's percentiles for each bin. Values can be `"both", "area" or "lines"` This can be thought of as a prediction interval about the `PI.real` or a confidence interval about the `PI`. However, note that with increasing number of simulations the CI will not go towards zero because the interval is also dependent on the size of the data set.
`PPI`	The plot prediction interval. Has a specific format that must be followed. See `setup.PPI`.
`PI.mean`	Should the mean be plotted in the VPCs? TRUE or FALSE.
`PI.delta.mean`	Should the delta mean be plotted in the VPCs? TRUE or FALSE.
`PI.x.median`	Should the x-location of percentile lines in a bin be marked at the median of the x-values? (`TRUE` or `FALSE`)
`PI.rug`	Should there be markings on the plot showing where the binning intervals for the VPC are (or the locations of the independent variable used for each VPC calculation if binning is not used)?
`PI.rug.col`	Color of the PI.rug.
`PI.rug.lwd`	Linw width of the PI.rug.
`PI.identify.outliers`	Should outlying percentiles of the real data be highlighted? (TRUE of FALSE)
`PI.outliers.col`	Color of PI.identify.outliers points
`PI.outliers.pch`	pch of PI.identify.outliers points
`PI.outliers.cex`	cex of PI.identify.outliers points
`PI.limits`	A vector of two values that describe the limits of the prediction interval that should be displayed. For example `c(0.025, 0.975)`. These limits should be found in the ‘PI.bin.table’ table. These limits are also used as the percentages for the `PI.real, PI.mirror` and `PI.ci`. However, the confidence interval in `PI.ci` is always the one defined in the `PI.bin.table`.
`PI.arcol`	The color of the `PI` area
`PI.up.lty`	The upper line type. can be "dotted" or "dashed", etc.
`PI.up.type`	The upper type used for plotting. Defaults to a line.
`PI.up.col`	The upper line color
`PI.up.lwd`	The upper line width
`PI.down.lty`	The lower line type. can be "dotted" or "dashed", etc.
`PI.down.type`	The lower type used for plotting. Defaults to a line.
`PI.down.col`	The lower line color
`PI.down.lwd`	The lower line width
`PI.med.lty`	The median line type. can be "dotted" or "dashed", etc.
`PI.med.type`	The median type used for plotting. Defaults to a line.
`PI.med.col`	The median line color
`PI.med.lwd`	The median line width
`PI.mean.lty`	The mean line type. can be "dotted" or "dashed", etc.
`PI.mean.type`	The mean type used for plotting. Defaults to a line.
`PI.mean.col`	The mean line color
`PI.mean.lwd`	The mean line width
`PI.delta.mean.lty`	The delta.mean line type. can be "dotted" or "dashed", etc.
`PI.delta.mean.type`	The delta.mean type used for plotting. Defaults to a line.
`PI.delta.mean.col`	The delta.mean line color
`PI.delta.mean.lwd`	The delta.mean line width
`PI.real.up.lty`	The upper line type. can be "dotted" or "dashed", etc.
`PI.real.up.type`	The upper type used for plotting. Defaults to a line.
`PI.real.up.col`	The upper line color
`PI.real.up.lwd`	The upper line width
`PI.real.down.lty`	The lower line type. can be "dotted" or "dashed", etc.
`PI.real.down.type`	The lower type used for plotting. Defaults to a line.
`PI.real.down.col`	The lower line color
`PI.real.down.lwd`	The lower line width
`PI.real.med.lty`	The median line type. can be "dotted" or "dashed", etc.
`PI.real.med.type`	The median type used for plotting. Defaults to a line.
`PI.real.med.col`	The median line color
`PI.real.med.lwd`	The median line width
`PI.real.mean.lty`	The mean line type. can be "dotted" or "dashed", etc.
`PI.real.mean.type`	The mean type used for plotting. Defaults to a line.
`PI.real.mean.col`	The mean line color
`PI.real.mean.lwd`	The mean line width
`PI.real.delta.mean.lty`	The delta.mean line type. can be "dotted" or "dashed", etc.
`PI.real.delta.mean.type`	The delta.mean type used for plotting. Defaults to a line.
`PI.real.delta.mean.col`	The delta.mean line color
`PI.real.delta.mean.lwd`	The delta.mean line width
`PI.mirror.up.lty`	The upper line type. can be "dotted" or "dashed", etc.
`PI.mirror.up.type`	The upper type used for plotting. Defaults to a line.
`PI.mirror.up.col`	The upper line color
`PI.mirror.up.lwd`	The upper line width
`PI.mirror.down.lty`	The lower line type. can be "dotted" or "dashed", etc.
`PI.mirror.down.type`	The lower type used for plotting. Defaults to a line.
`PI.mirror.down.col`	The lower line color
`PI.mirror.down.lwd`	The lower line width
`PI.mirror.med.lty`	The median line type. can be "dotted" or "dashed", etc.
`PI.mirror.med.type`	The median type used for plotting. Defaults to a line.
`PI.mirror.med.col`	The median line color
`PI.mirror.med.lwd`	The median line width
`PI.mirror.mean.lty`	The mean line type. can be "dotted" or "dashed", etc.
`PI.mirror.mean.type`	The mean type used for plotting. Defaults to a line.
`PI.mirror.mean.col`	The mean line color
`PI.mirror.mean.lwd`	The mean line width
`PI.mirror.delta.mean.lty`	The delta.mean line type. can be "dotted" or "dashed", etc.
`PI.mirror.delta.mean.type`	The delta.mean type used for plotting. Defaults to a line.
`PI.mirror.delta.mean.col`	The delta.mean line color
`PI.mirror.delta.mean.lwd`	The delta.mean line width
`PI.ci.up.arcol`	The color of the upper `PI.ci`.
`PI.ci.up.lty`	The upper line type. can be "dotted" or "dashed", etc.
`PI.ci.up.type`	The upper type used for plotting. Defaults to a line.
`PI.ci.up.col`	The upper line color
`PI.ci.up.lwd`	The upper line width
`PI.ci.down.arcol`	The color of the lower `PI.ci`.
`PI.ci.down.lty`	The lower line type. can be "dotted" or "dashed", etc.
`PI.ci.down.type`	The lower type used for plotting. Defaults to a line.
`PI.ci.down.col`	The lower line color
`PI.ci.down.lwd`	The lower line width
`PI.ci.med.arcol`	The color of the median `PI.ci`.
`PI.ci.med.lty`	The median line type. can be "dotted" or "dashed", etc.
`PI.ci.med.type`	The median type used for plotting. Defaults to a line.
`PI.ci.med.col`	The median line color
`PI.ci.med.lwd`	The median line width
`PI.ci.mean.arcol`	The color of the mean `PI.ci`.
`PI.ci.mean.lty`	The mean line type. can be "dotted" or "dashed", etc.
`PI.ci.mean.type`	The mean type used for plotting. Defaults to a line.
`PI.ci.mean.col`	The mean line color
`PI.ci.mean.lwd`	The mean line width
`PI.ci.delta.mean.arcol`	The color of the delta.mean `PI.ci`.
`PI.ci.delta.mean.lty`	The delta.mean line type. can be "dotted" or "dashed", etc.
`PI.ci.delta.mean.type`	The delta.mean type used for plotting. Defaults to a line.
`PI.ci.delta.mean.col`	The delta.mean line color
`PI.ci.delta.mean.lwd`	The delta.mean line width
`PI.ci.area.smooth`	Should the "area" for `PI.ci` be smoothed to match the "lines" argument? Allowed values are `TRUE/FALSE`. The "area" is set by default to show the bins used in the `PI.ci` computation. By smoothing, information is lost and, in general, the confidence intervals will be smaller than they are in reality.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`col`	The color for lines and points. Specified as an integer or a text string. A full list is obtained by the R command `colours()`. The default is blue (col=4).
`pch`	The plotting character, or symbol, to use. Specified as an integer. See R help on `points`. The default is an open circle.
`cex`	The amount by which plotting text and symbols should be scaled relative to the default. 'NULL' and 'NA' are equivalent to '1.0'.
`lty`	The line type. Line types can either be specified as an integer (0=blank, 1=solid, 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) or as one of the character strings '"blank"', '"solid"', '"dashed"', '"dotted"', '"dotdash"', '"longdash"', or '"twodash"', where '"blank"' uses 'invisible lines' (i.e., doesn't draw them).
`lwd`	the width for lines. Specified as an integer. The default is 1.
`fill`	fill for areas in plot
`ids`	Logical value specifying whether to label data points.
`idsmode`	Determines the way text labels are added to plots. `NULL` means that only extreme points are labelled. Non-`NULL` means all data points are labelled. (See `link{xpose.plot.default}`)
`idsext`	specifies the extent of the extremes to be used in labelling points. The default is 0.05 (only the most extreme 5% of points are labelled).
`idscex`	the amount by which labels should be scaled relative to the default. 'NULL' and 'NA' are equivalent to '1.0'.
`idsdir`	a string indicating the directions of the extremes to include in labelling. Possible values are "up", "down" and "both".
`abline`	Vector of arguments to the `panel.abline` function. No abline is drawn if `NULL`.
`abllwd`	Line width of any abline.
`abllty`	Line type of any abline.
`ablcol`	Line colour of any abline.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`smlwd`	Line width of the x-y smooth.
`smlty`	Line type of the x-y smooth.
`smcol`	Line color of the x-y smooth.
`smspan`	The smoothness parameter for the x-y smooth. The default is 0.667. An argument to `panel.loess`.
`smdegr`	The degree of the polynomials to be used for the x-y smooth, up to 2. The default is 1. An argument to `panel.loess`.
`smooth.for.groups`	Should a smooth for each group be drawn?
`lmline`	logical variable specifying whether a linear regression line should be superimposed over an `xyplot`. `NULL` ~ FALSE. (`y~x`)
`lmlwd`	Line width of the lmline.
`lmlty`	Line type of the lmline.
`lmcol`	Line colour of the lmline.
`suline`	A `NULL` value indicates that no superposed line should be added to the graph. If non-`NULL` then this should be the vector (the same length as y) of data points to be used for the smoothed superposed line.
`sulwd`	Line width of the superposed smooth.
`sulty`	Line type of the superposed smooth.
`sucol`	Line color of the superposed smooth.
`suspan`	The smoothness parameter. The default is 0.667. An argument to `panel.loess`.
`sudegr`	The degree of the polynomials to be used, up to 2. The default is 1. An argument to `panel.loess`.
`grid`	logical value indicating whether a visual reference grid should be added to the graph. (Could use arguments for line type, color etc).
`logy`	Logical value indicating whether the y-axis should be logarithmic.
`logx`	Logical value indicating whether the y-axis should be logarithmic.
`force.x.continuous`	Logical value indicating whether x-values should be taken as continuous, even if categorical.
`bwhoriz`	logical value indicating whether box and whiskers should be horizontal or not. The default is FALSE.
`bwratio`	Ratio of box height to inter-box space. The default is 1.5. An argument for `panel.bwplot`.
`bwvarwid`	Logical. If TRUE, widths of boxplots are proportional to the number of points used in creating it. The default is FALSE. An argument for `panel.bwplot`.
`bwdotpch`	Graphical parameter controlling the dot plotting character in boxplots. 'bwdotpch="\|"' is treated specially, by replacing the dot with a line. The default is 16. An argument for `panel.bwplot`.
`bwdotcol`	Graphical parameter controlling the dot colour in boxplots - an integer or string. See 'col'. The default is black. An argument for `panel.bwplot`.
`bwdotcex`	The amount by which plotting text and symbols should be scaled relative to the default in boxplots. 'NULL' and 'NA' are equivalent to '1.0'. An argument for `panel.bwplot`.
`bwreccol`	The colour to use for the box rectangle in boxplots - an integer or string. The default is blue. See `trellis.par.get` and "box.rectangle".
`bwrecfill`	The colour to use for filling the box rectangle in boxplots - an integer or string. The default is transparent (none). See `trellis.par.get` and "box.rectangle".
`bwreclty`	The line type for the box rectangle in boxplots - an integer or string. The default is solid. See `trellis.par.get` and "box.rectangle".
`bwreclwd`	The width of the lines for the box rectangle in boxplots - an integer. The default is 1. See `trellis.par.get` and "box.rectangle".
`bwumbcol`	The colour to use for the umbrellas in boxplots - an integer or string. The default is blue. See `trellis.par.get` and "box.umbrella".
`bwumblty`	The line type for the umbrellas in boxplots - an integer or string. The default is solid.See `trellis.par.get` and "box.umbrella".
`bwumblwd`	the width of the lines for the umbrellas in boxplots - an integer. The default is 1. See `trellis.par.get` and "box.umbrella".
`bwoutcol`	The colour to use for the outliers in boxplots - an integer or string. The default is blue. See `trellis.par.get` and "box.symbol".
`bwoutcex`	The amount by which outlier points should be scaled relative to the default in boxplots. 'NULL' and 'NA' are equivalent to '1.0'. The default is 0.8. See `trellis.par.get` and "box.symbol".
`bwoutpch`	The plotting character, or symbol, to use for outlier points in boxplots. Specified as an integer. See R help on 'points'. The default is an open circle. See `trellis.par.get` and "box.symbol".
`autocorr`	Is this an autocorrelation plot? Values can be `TRUE/FALSE`.
`vline`	Add a vertical line to the plot at the values specified.
`vllwd`	Width (lwd) of vertical line
`vllty`	Line type (lty) for vertical line
`vlcol`	Color (col) of vertical line
`hline`	Add a horizontal line to the plot at the values specified.
`hllwd`	Width (lwd) of horizontal line
`hllty`	Line type (lty) for horizontal line
`hlcol`	Color (col) of horizontal line
`pch.ip.sp`	If there is a panel with just one observation then this specifies the type of points for the DV, IPRED and PRED respectively.
`cex.ip.sp`	If there is a panel with just one observation then this specifies the size of the points for the DV, IPRED and PRED respectively.
`...`	Other arguments that may be needed in the function.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Justin Wilkins and Andrew Hooker

Default histogram panel function for Xpose 4

Description

This is the histogram panel function for Xpose 4. This is not intended to be ised outside the xpose.plot.histogram function. Most of the arguments take their default values from xpose.data object but this can be overridden by supplying them as argument to xpose.plot.histogram.

Usage

xpose.panel.histogram(
  x,
  object,
  breaks = NULL,
  dens = TRUE,
  hidlty = object@[email protected]$hidlty,
  hidcol = object@[email protected]$hidcol,
  hidlwd = object@[email protected]$hidlwd,
  hiborder = object@[email protected]$hiborder,
  hilty = object@[email protected]$hilty,
  hicol = object@[email protected]$hicol,
  hilwd = object@[email protected]$hilwd,
  math.dens = NULL,
  vline = NULL,
  vllwd = 3,
  vllty = 1,
  vlcol = "grey",
  hline = NULL,
  hllwd = 3,
  hllty = 1,
  hlcol = "grey",
  bins.per.panel.equal = TRUE,
  showMean = FALSE,
  meanllwd = 3,
  meanllty = 1,
  meanlcol = "orange",
  showMedian = FALSE,
  medianllwd = 3,
  medianllty = 1,
  medianlcol = "black",
  showPCTS = FALSE,
  PCTS = c(0.025, 0.975),
  PCTSllwd = 2,
  PCTSllty = hidlty,
  PCTSlcol = "black",
  vdline = NULL,
  vdllwd = 3,
  vdllty = 1,
  vdlcol = "red",
  ...,
  groups
)
xpose.panel.histogram(
  x,
  object,
  breaks = NULL,
  dens = TRUE,
  hidlty = object@Prefs@Graph.prefs$hidlty,
  hidcol = object@Prefs@Graph.prefs$hidcol,
  hidlwd = object@Prefs@Graph.prefs$hidlwd,
  hiborder = object@Prefs@Graph.prefs$hiborder,
  hilty = object@Prefs@Graph.prefs$hilty,
  hicol = object@Prefs@Graph.prefs$hicol,
  hilwd = object@Prefs@Graph.prefs$hilwd,
  math.dens = NULL,
  vline = NULL,
  vllwd = 3,
  vllty = 1,
  vlcol = "grey",
  hline = NULL,
  hllwd = 3,
  hllty = 1,
  hlcol = "grey",
  bins.per.panel.equal = TRUE,
  showMean = FALSE,
  meanllwd = 3,
  meanllty = 1,
  meanlcol = "orange",
  showMedian = FALSE,
  medianllwd = 3,
  medianllty = 1,
  medianlcol = "black",
  showPCTS = FALSE,
  PCTS = c(0.025, 0.975),
  PCTSllwd = 2,
  PCTSllty = hidlty,
  PCTSlcol = "black",
  vdline = NULL,
  vdllwd = 3,
  vdllty = 1,
  vdlcol = "red",
  ...,
  groups
)

Arguments

`x`	Name(s) of the x-variable.
`object`	An xpose.data object.
`breaks`	The breakpoints for the histogram.
`dens`	Density plot on top of histogram?
`hidlty`	Density line type.
`hidcol`	Color of density line.
`hidlwd`	Width of density line.
`hiborder`	Colour of the bar borders.
`hilty`	Line type for the bar borders.
`hicol`	Fill colour for the bars.
`hilwd`	Width for the bar borders.
`math.dens`	Should a density line be drawn. Values are `NULL` or `TRUE`.
`vline`	`NULL` or a vector of locations for the vertical lines to be drawn. For example, `vline=c(50,60)` will draw two vertical lines. The function `panel.abline` is used.
`vllwd`	Line width of the vertical lines defined with `vline`. Can be a vector or a single value, for example `vllwd=2` or `vllwd=c(2,3)`.
`vllty`	Line type of the vertical lines defined with `vline`. Can be a vector or a single value, for example `vllty=1` or `vllty=c(1,2)`.
`vlcol`	Line color of the vertical lines defined with `vline`. Can be a vector or a single value, for example `vlcol="red"` or `vllty=c("red","blue")`.
`hline`	`NULL` or a vector of locations for the horizontal lines to be drawn. For example, `hline=c(50,60)` will draw two horizontal lines. The function `panel.abline` is used.
`hllwd`	Line width of the horizontal lines defined with `hline`. Can be a vector or a single value, for example `hllwd=2` or `hllwd=c(2,3)`.
`hllty`	Line type of the horizontal lines defined with `hline`. Can be a vector or a single value, for example `hllty=1` or `hllty=c(1,2)`.
`hlcol`	Line color of the horizontal lines defined with `hline`. Can be a vector or a single value, for example `hlcol="red"` or `hllty=c("red","blue")`.
`bins.per.panel.equal`	Allow for different bins in different panels for continuous data? TRUE or FALSE.
`showMean`	Should the mean of the data in the histogram be shown?
`meanllwd`	Line width of mean line.
`meanllty`	The line type for the mean
`meanlcol`	Color for the mean line
`showMedian`	Should the median of the data for the histogram be shown as a vertical line?
`medianllwd`	line width of median line.
`medianllty`	line type of median line.
`medianlcol`	color of median line.
`showPCTS`	Should percentiles of the data for the histogram be shown?
`PCTS`	A vector of percentiles to show. Can be any length.
`PCTSllwd`	line width of percentiles. Can be a vector of same length as `PCTS`.
`PCTSllty`	Line type of the percentiles. Can be a vector of same length as `PCTS`.
`PCTSlcol`	Color of the percentiles. Can be a vector of same length as `PCTS`.
`vdline`	vertical line different for each histogram. Must be a vector.
`vdllwd`	line widths
`vdllty`	line types
`vdlcol`	line colors
`...`	Other arguments that may be needed in the function.
`groups`	used to pass the conditioning variable into this function.

Author(s)

Andrew Hooker, Mats Karlsson, Justin Wilkins & E. Niclas Jonsson

Default QQ panel function for Xpose 4

Description

This is the QQ panel function for Xpose 4. This is not intended to be used outside the xpose.plot.qq function. Most of the arguments take their default values from xpose.data object but this can be overridden by supplying them as argument to xpose.plot.qq.

Usage

xpose.panel.qq(
  x,
  object,
  pch = object@[email protected]$pch,
  col = object@[email protected]$col,
  cex = object@[email protected]$cex,
  abllty = object@[email protected]$abllty,
  abllwd = object@[email protected]$abllwd,
  ablcol = object@[email protected]$ablcol,
  grid = object@[email protected]$grid,
  ...
)
xpose.panel.qq(
  x,
  object,
  pch = object@Prefs@Graph.prefs$pch,
  col = object@Prefs@Graph.prefs$col,
  cex = object@Prefs@Graph.prefs$cex,
  abllty = object@Prefs@Graph.prefs$abllty,
  abllwd = object@Prefs@Graph.prefs$abllwd,
  ablcol = object@Prefs@Graph.prefs$ablcol,
  grid = object@Prefs@Graph.prefs$grid,
  ...
)

Arguments

`x`	Name(s) of the x-variable.
`object`	An xpose.data object.
`pch`	Plot character to use.
`col`	Colour of lines and plot symbols.
`cex`	Amount to scale the plotting character by.
`abllty`	Line type.
`abllwd`	Line width.
`ablcol`	Line colour.
`grid`	logical value indicating whether a visual reference grid should be added to the graph. (Could use arguments for line type, color etc).
`...`	Other arguments that may be needed in the function.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Scatterplot matrix panel function for Xpose 4

Description

This is the scatterplot matrix panel function for Xpose 4. This is not intended to be ised outside the xpose.plot.splom function. Most of the arguments take their default values from xpose.data object but this can be overridden by supplying them as argument to xpose.plot.splom.

Usage

xpose.panel.splom(
  x,
  y,
  object,
  subscripts,
  onlyfirst = TRUE,
  inclZeroWRES = FALSE,
  type = "p",
  col = object@[email protected]$col,
  pch = object@[email protected]$pch,
  cex = object@[email protected]$cex,
  lty = object@[email protected]$lty,
  lwd = object@[email protected]$lwd,
  smooth = TRUE,
  smlwd = object@[email protected]$smlwd,
  smlty = object@[email protected]$smlty,
  smcol = object@[email protected]$smcol,
  smspan = object@[email protected]$smspan,
  smdegr = object@[email protected]$smdegr,
  lmline = NULL,
  lmlwd = object@[email protected]$lmlwd,
  lmlty = object@[email protected]$lmlty,
  lmcol = object@[email protected]$lmcol,
  grid = object@[email protected]$grid,
  groups = NULL,
  ...
)
xpose.panel.splom(
  x,
  y,
  object,
  subscripts,
  onlyfirst = TRUE,
  inclZeroWRES = FALSE,
  type = "p",
  col = object@Prefs@Graph.prefs$col,
  pch = object@Prefs@Graph.prefs$pch,
  cex = object@Prefs@Graph.prefs$cex,
  lty = object@Prefs@Graph.prefs$lty,
  lwd = object@Prefs@Graph.prefs$lwd,
  smooth = TRUE,
  smlwd = object@Prefs@Graph.prefs$smlwd,
  smlty = object@Prefs@Graph.prefs$smlty,
  smcol = object@Prefs@Graph.prefs$smcol,
  smspan = object@Prefs@Graph.prefs$smspan,
  smdegr = object@Prefs@Graph.prefs$smdegr,
  lmline = NULL,
  lmlwd = object@Prefs@Graph.prefs$lmlwd,
  lmlty = object@Prefs@Graph.prefs$lmlty,
  lmcol = object@Prefs@Graph.prefs$lmcol,
  grid = object@Prefs@Graph.prefs$grid,
  groups = NULL,
  ...
)

Arguments

`x`	Name(s) of the x-variable.
`y`	Name(s) of the y-variable.
`object`	An xpose.data object.
`subscripts`	The standard Trellis subscripts argument (see `xyplot`)
`onlyfirst`	Logical value indicating whether only the first row per individual is included in the plot.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot.
`type`	1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
`col`	The color for lines and points. Specified as an integer or a text string. A full list is obtained by the R command `colours()`. The default is blue (col=4).
`pch`	The plotting character, or symbol, to use. Specified as an integer. See R help on `points`. The default is an open circle.
`cex`	The amount by which plotting text and symbols should be scaled relative to the default. 'NULL' and 'NA' are equivalent to '1.0'.
`lty`	The line type. Line types can either be specified as an integer (0=blank, 1=solid, 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) or as one of the character strings '"blank"', '"solid"', '"dashed"', '"dotted"', '"dotdash"', '"longdash"', or '"twodash"', where '"blank"' uses 'invisible lines' (i.e., doesn't draw them).
`lwd`	the width for lines. Specified as an integer. The default is 1.
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`smlwd`	Line width of the x-y smooth.
`smlty`	Line type of the x-y smooth.
`smcol`	Line color of the x-y smooth.
`smspan`	The smoothness parameter for the x-y smooth. The default is 0.667. An argument to `panel.loess`.
`smdegr`	The degree of the polynomials to be used for the x-y smooth, up to 2. The default is 1. An argument to `panel.loess`.
`lmline`	logical variable specifying whether a linear regression line should be superimposed over an `xyplot`. `NULL` ~ FALSE. (`y~x`)
`lmlwd`	Line width of the lmline.
`lmlty`	Line type of the lmline.
`lmcol`	Line colour of the lmline.
`grid`	logical value indicating whether a visual reference grid should be added to the graph. (Could use arguments for line type, color etc).
`groups`	Name of the variable used for superpose plots.
`...`	Other arguments that may be needed in the function.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

The generic Xpose functions for box-and-whisker plots

Description

This is a wrapper function for the lattice bwplot function.

Usage

xpose.plot.bw(
  x,
  y,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  panel = xpose.panel.bw,
  groups = NULL,
  ids = FALSE,
  logy = FALSE,
  logx = FALSE,
  aspect = object@[email protected]$aspect,
  funy = NULL,
  funx = NULL,
  PI = FALSE,
  by = object@[email protected]$condvar,
  force.by.factor = FALSE,
  ordby = object@[email protected]$ordby,
  byordfun = object@[email protected]$byordfun,
  shingnum = object@[email protected]$shingnum,
  shingol = object@[email protected]$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  subset = xsubset(object),
  main = xpose.create.title(x, y, object, subset, funx, funy, ...),
  xlb = xpose.create.label(x, object, funx, logx, ...),
  ylb = xpose.create.label(y, object, funy, logy, ...),
  scales = list(),
  suline = object@[email protected]$suline,
  binvar = NULL,
  bins = 10,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)
xpose.plot.bw(
  x,
  y,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  panel = xpose.panel.bw,
  groups = NULL,
  ids = FALSE,
  logy = FALSE,
  logx = FALSE,
  aspect = object@Prefs@Graph.prefs$aspect,
  funy = NULL,
  funx = NULL,
  PI = FALSE,
  by = object@Prefs@Graph.prefs$condvar,
  force.by.factor = FALSE,
  ordby = object@Prefs@Graph.prefs$ordby,
  byordfun = object@Prefs@Graph.prefs$byordfun,
  shingnum = object@Prefs@Graph.prefs$shingnum,
  shingol = object@Prefs@Graph.prefs$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  subset = xsubset(object),
  main = xpose.create.title(x, y, object, subset, funx, funy, ...),
  xlb = xpose.create.label(x, object, funx, logx, ...),
  ylb = xpose.create.label(y, object, funy, logy, ...),
  scales = list(),
  suline = object@Prefs@Graph.prefs$suline,
  binvar = NULL,
  bins = 10,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)

Arguments

`x`	Name(s) of the x-variable.
`y`	Name(s) of the y-variable.
`object`	An xpose.data object.
`inclZeroWRES`	A logical value indicating whether rows with WRES=0 should be plotted.
`onlyfirst`	A logical value indicating whether only the first row per individual should be included in the plot.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`panel`	The name of the panel function to use. This should in most cases be left as `xpose.panel.bw`.
`groups`	A string with the name of any grouping variable (used as the groups argument to `panel.xyplot`.
`ids`	A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` Xpose data variable).
`logy`	Logical value indicating whether the y-axis should be logarithmic.
`logx`	Logical value indicating whether the x-axis should be logarithmic.
`aspect`	The aspect ratio of the display (see `bwplot`).
`funy`	String with the name of a function to apply to the y-variable before plotting, e.g. "abs".
`funx`	String with the name of a function to apply to the x-variable before plotting, e.g. "abs".
`PI`	Either "lines", "area" or "both" specifying whether prediction intervals (as lines, as a shaded area or both) should be computed from the data in `SData` and added to the display. `NULL` means no prediction interval.
`by`	A string or a vector of strings with the name(s) of the conditioning variables.
`force.by.factor`	Logical value. If TRUE, and `by` is not `NULL`, the variable specified by `by` is taken as categorical.
`ordby`	A string with the name of a variable to be used to reorder any factor conditioning variables (`by`). The variable is used in a call to the `reorder` function.
`byordfun`	The name of the function to be used when reordering a factor conditioning variable (see argument `ordby`).
`shingnum`	The number of shingles ("parts") a continuous conditioning variable should be divided into.
`shingol`	The amount of overlap between adjacent shingles (see argument `shingnum`)
`strip`	The name of the function to be used as the strip argument to the `bwplot`.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`main`	A string giving the plot title or `NULL` if none.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`scales`	A list to be used for the `scales` argument in `bwplot`.
`suline`	A string giving the variable to be used to construct a smooth to superpose on the display. `NULL` if none. This argument is used if you want to add a superpose line of a variable not present in the `y` list of variables.
`binvar`	Variable to be used for binning.
`bins`	The number of bins to be used. The default is 10.
`mirror`	Should we create mirror plots from simulation data? Value can be `FALSE`, `TRUE` or `1` for one mirror plot, or `3` for three mirror plots.
`max.plots.per.page`	The maximum number of plots per page that can be created with the mirror plots.
`mirror.aspect`	The aspect ratio of the plots used for mirror functionality.
`pass.plot.list`	Should we pass the list of plots created with mirror or should we print them directly. Values can be `TRUE/FALSE`.
`x.cex`	The size of the x-axis label.
`y.cex`	The size of the y-axis label.
`main.cex`	The size of the title.
`mirror.internal`	an internal mirror argument used in `create.mirror`. Checks if the `strip` argument from `bwplot` has been used.
`...`	Other arguments passed to `xpose.panel.bw`.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Box & whisker plot of WRES vs PRED
xpose.plot.bw("WRES", "PRED", xpdb5, binvar="PRED")

## End(Not run)

## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Box & whisker plot of WRES vs PRED
xpose.plot.bw("WRES", "PRED", xpdb5, binvar="PRED")

## End(Not run)

The Xpose 4 generic functions for continuous y-variables.

Description

This function is a wrapper for the lattice xyplot function.

Usage

xpose.plot.default(
  x,
  y,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  panel = xpose.panel.default,
  groups = object@Prefs@Xvardef$id,
  ids = object@[email protected]$ids,
  logy = FALSE,
  logx = FALSE,
  yscale.components = "default",
  xscale.components = "default",
  aspect = object@[email protected]$aspect,
  funx = NULL,
  funy = NULL,
  iplot = NULL,
  PI = NULL,
  by = object@[email protected]$condvar,
  force.by.factor = FALSE,
  ordby = object@[email protected]$ordby,
  byordfun = object@[email protected]$byordfun,
  shingnum = object@[email protected]$shingnum,
  shingol = object@[email protected]$shingol,
  by.interval = NULL,
  strip = function(...) {
     strip.default(..., strip.names = c(TRUE, TRUE))
 },
  use.xpose.factor.strip.names = TRUE,
  subset = xsubset(object),
  autocorr = FALSE,
  main = xpose.create.title(x, y, object, subset, funx, funy, ...),
  xlb = xpose.create.label(x, object, funx, logx, autocorr.x = autocorr, ...),
  ylb = xpose.create.label(y, object, funy, logy, autocorr.y = autocorr, ...),
  scales = list(),
  suline = object@[email protected]$suline,
  bwhoriz = object@[email protected]$bwhoriz,
  dilution = FALSE,
  dilfrac = object@[email protected]$dilfrac,
  diltype = object@[email protected]$diltype,
  dilci = object@[email protected]$dilci,
  seed = NULL,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)
xpose.plot.default(
  x,
  y,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  panel = xpose.panel.default,
  groups = object@Prefs@Xvardef$id,
  ids = object@Prefs@Graph.prefs$ids,
  logy = FALSE,
  logx = FALSE,
  yscale.components = "default",
  xscale.components = "default",
  aspect = object@Prefs@Graph.prefs$aspect,
  funx = NULL,
  funy = NULL,
  iplot = NULL,
  PI = NULL,
  by = object@Prefs@Graph.prefs$condvar,
  force.by.factor = FALSE,
  ordby = object@Prefs@Graph.prefs$ordby,
  byordfun = object@Prefs@Graph.prefs$byordfun,
  shingnum = object@Prefs@Graph.prefs$shingnum,
  shingol = object@Prefs@Graph.prefs$shingol,
  by.interval = NULL,
  strip = function(...) {
     strip.default(..., strip.names = c(TRUE, TRUE))
 },
  use.xpose.factor.strip.names = TRUE,
  subset = xsubset(object),
  autocorr = FALSE,
  main = xpose.create.title(x, y, object, subset, funx, funy, ...),
  xlb = xpose.create.label(x, object, funx, logx, autocorr.x = autocorr, ...),
  ylb = xpose.create.label(y, object, funy, logy, autocorr.y = autocorr, ...),
  scales = list(),
  suline = object@Prefs@Graph.prefs$suline,
  bwhoriz = object@Prefs@Graph.prefs$bwhoriz,
  dilution = FALSE,
  dilfrac = object@Prefs@Graph.prefs$dilfrac,
  diltype = object@Prefs@Graph.prefs$diltype,
  dilci = object@Prefs@Graph.prefs$dilci,
  seed = NULL,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)

Arguments

`x`	A string or a vector of strings with the name(s) of the x-variable(s).
`y`	A string or a vector of strings with the name(s) of the y-variable(s).
`object`	An "xpose.data" object.
`inclZeroWRES`	A logical value indicating whether rows with WRES=0 should be plotted.
`onlyfirst`	A logical value indicating whether only the first row per individual should be included in the plot.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`panel`	The name of the panel function to use.
`groups`	A string with the name of any grouping variable (used as the groups argument to `panel.xyplot`.
`ids`	A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` xpose data variable).
`logy`	Logical value indicating whether the y-axis should be logarithmic.
`logx`	Logical value indicating whether the x-axis should be logarithmic.
`yscale.components`	Used to change the way the axis look if `logy` is used. Can be a user defined function or `link{xpose.yscale.components.log10}`. If the axes are not log transformed then `yscale.components.default` is used.
`xscale.components`	Used to change the way the axis look if `logx` is used. Can be a user defined function or `link{xpose.xscale.components.log10}`. If the axes are not log transformed then `xscale.components.default` is used.
`aspect`	The aspect ratio of the display (see `xyplot`).
`funx`	String with the name of a function to apply to the x-variable before plotting, e.g. "abs".
`funy`	String with the name of a function to apply to the y-variable before plotting, e.g. "abs".
`iplot`	Is this an individual plots matrix? Internal use only.
`PI`	Either "lines", "area" or "both" specifying whether prediction intervals (as lines, as a shaded area or both) should be computed from the data in `SData` and added to the display. `NULL` means no prediction interval.
`by`	A string or a vector of strings with the name(s) of the conditioning variables.
`force.by.factor`	Logical value. If TRUE, and `by` is not `NULL`, the variable specified by `by` is taken as categorical.
`ordby`	A string with the name of a variable to be used to reorder any factor conditioning variables (`by`). The variable is used in a call to the `reorder.factor` function.
`byordfun`	The name of the function to be used when reordering a factor conditioning variable (see argument `ordby`)
`shingnum`	The number of shingles ("parts") a continuous conditioning variable should be divided into.
`shingol`	The amount of overlap between adjacent shingles (see argument `shingnum`)
`by.interval`	The intervals to use for conditioning on a continuous variable with `by`.
`strip`	The name of the function to be used as the strip argument to the `xyplot`. An easy way to change the strip appearance is to use `strip.custom`. For example, if you want to change the text in the strips you can use `strip=strip.custom(factor.levels=c("Hi","There"))` if the `by` variable is a factor and `strip=strip.custom(var.name=c("New Name"))` if the `by` variable is continuous.
`use.xpose.factor.strip.names`	Use factor names in strips of conditioning plots..
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`autocorr`	Is this an autocorrelation plot? Values can be `TRUE/FALSE`.
`main`	A string giving the plot title or `NULL` if none.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`scales`	A list to be used for the `scales` argument in `xyplot`.
`suline`	A string giving the variable to be used to construct a smooth to superpose on the display. `NULL` if none. This argument is used if you want to add a superpose line of a variable not present in the `y` list of variables.
`bwhoriz`	A logical value indicating if box and whiskers bars should be plotted horizontally or not. Used when the x-variable(s) is categorical.
`dilution`	Logical value indicating whether data dilution should be used.
`dilfrac`	Dilution fraction indicating the expected fraction of individuals to display in the plots. The exact meaning depends on the type of dilution (see below).
`diltype`	Indicating what type of dilution to apply. `NULL` means random dilution without stratification. A non`NULL` value means stratified dilution.
`dilci`	A number between 0 and 1 giving the range eligible for dilution in a stratified dilution (see below).
`seed`	Seed number used for random dilution. `NULL` means no seed.
`mirror`	Should we create mirror plots from simulation data? Value can be `FALSE`, `TRUE` or `1` for one mirror plot, or `3` for three mirror plots.
`max.plots.per.page`	The maximum number of plots per page that can be created with the mirror plots.
`mirror.aspect`	The aspect ratio of the plots used for mirror functionality.
`pass.plot.list`	Should we pass the list of plots created with mirror or should we print them directly. Values can be `TRUE/FALSE`.
`x.cex`	The size of the x-axis label.
`y.cex`	The size of the y-axis label.
`main.cex`	The size of the title.
`mirror.internal`	an internal mirror argument used in `create.mirror`. Checks if the `strip` argument from `xyplot` has been used.
`...`	Other arguments passed to `xpose.panel.default`.

Details

y must be numeric (continuous) while x can be either numeric of factor. If x is numeric then a regular xy-plot is drawn. If x is a factor, on the other hand, a box and whiskers plot is constructed.

x and y can be either single valued strings or vector of strings. x and y can not both be vectors in the same call to the function.

If ids is TRUE, text labels are added to the plotting symbols. The labels are taken from the idlab xpose data variable. The way the text labels are plotted is governed by the idsmode argument (passed down to the panel function). idsmode=NULL (the default) means that only extreme data points are labelled while a non-NULL value adds labels to all data points (the default in Xpose 3). xpose.panel.default identifies extreme data points by fitting a loess smooth (y~x) and looking at the residuals from that fit. Points that are associated with the highest/lowest residuals are labelled. "High" and "low" are judged by the panel function parameter idsext, which gives the fraction of the total number of data points that are to be judged extreme in the "up" and "down" direction. The default value for idsext is 0.05 (see xpose.prefs-class). There is also a possibility to label only the high or low extreme points. This is done through the idsdir argument to xpose.panel.default. A value of "both" (the default) means that both high and low extreme points are labelled while "up" and "down" labels the high and low extreme points respectively.

Data dilution is useful is situations when there is an excessive amount of data. xpose.plot.default can dilute data in two different ways. The first is a completely random dilution in which all individuals are eligible for exclusion from the plot. In this case the argument dilfrac determines the fraction of individuals that are excluded from the plot. The second type of dilution uses stratification to make sure that none of the extreme individuals are omitted from the plot. Extreme individuals are identified in a similar manner as extreme data points are identified for text labelling. A smooth is fitted to the data and the extreme residuals from that fit is used to inform about extremeness. What is judged as extreme is determined by the argument dilci, which defaults to 0.95 (Note that the meaning of this is the opposite to idsext). dilci give the confidence level of the interval around the fitted curve outside of which points are deemed to be extreme. Extreme individuals are those that have at least one point in the "extremeness" interval. Individuals that do not have any extreme points are eligible for dilution and dilfrac give the number of these that should be omitted from the graph. This means that dilfrac should usually be grater for stratified dilution than in completely random dilution. Any smooths added to a diluted plot is based on undiluted data.

More graphical parameters may be passed to xpose.panel.default.

Value

Returns a xyplot graph object.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## A spaghetti plot of DV vs TIME
xpose.plot.default("TIME", "DV", xpdb5) 

## A conditioning plot
xpose.plot.default("TIME", "DV", xpdb5, by = "SEX")
 
## Multiple x-variables
xpose.plot.default(c("WT", "SEX"), "CL", xpdb5)

## Multiple y-variables
xpose.plot.default("WT", c("CL", "V"), xpdb5)
xpose.plot.default("WT", c("CL", "V"), xpdb5, by=c("SEX", "HCTZ"))

## determining the interval for the conditioning variable
wt.ints <- matrix(c(50,60,60,70,70,80,80,90,90,100,100,150),nrow=6,ncol=2,byrow=T)
xpose.plot.default("TIME","DV",xpdb5,by="WT", by.interval=wt.ints)

## End(Not run)


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## A spaghetti plot of DV vs TIME
xpose.plot.default("TIME", "DV", xpdb5) 

## A conditioning plot
xpose.plot.default("TIME", "DV", xpdb5, by = "SEX")
 
## Multiple x-variables
xpose.plot.default(c("WT", "SEX"), "CL", xpdb5)

## Multiple y-variables
xpose.plot.default("WT", c("CL", "V"), xpdb5)
xpose.plot.default("WT", c("CL", "V"), xpdb5, by=c("SEX", "HCTZ"))

## determining the interval for the conditioning variable
wt.ints <- matrix(c(50,60,60,70,70,80,80,90,90,100,100,150),nrow=6,ncol=2,byrow=T)
xpose.plot.default("TIME","DV",xpdb5,by="WT", by.interval=wt.ints)

## End(Not run)

The Xpose 4 generic functions for continuous y-variables.

Description

This function is a wrapper for the lattice xyplot function.

Usage

xpose.plot.histogram(
  x,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  type = "density",
  aspect = object@[email protected]$aspect,
  scales = list(),
  by = object@[email protected]$condvar,
  force.by.factor = FALSE,
  ordby = object@[email protected]$ordby,
  byordfun = object@[email protected]$byordfun,
  shingnum = object@[email protected]$shingnum,
  shingol = object@[email protected]$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  subset = xsubset(object),
  main = xpose.create.title.hist(x, object, subset, ...),
  xlb = NULL,
  ylb = "Density",
  hicol = object@[email protected]$hicol,
  hilty = object@[email protected]$hilty,
  hilwd = object@[email protected]$hilwd,
  hidcol = object@[email protected]$hidcol,
  hidlty = object@[email protected]$hidlty,
  hidlwd = object@[email protected]$hidlwd,
  hiborder = object@[email protected]$hiborder,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)
xpose.plot.histogram(
  x,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  type = "density",
  aspect = object@Prefs@Graph.prefs$aspect,
  scales = list(),
  by = object@Prefs@Graph.prefs$condvar,
  force.by.factor = FALSE,
  ordby = object@Prefs@Graph.prefs$ordby,
  byordfun = object@Prefs@Graph.prefs$byordfun,
  shingnum = object@Prefs@Graph.prefs$shingnum,
  shingol = object@Prefs@Graph.prefs$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  subset = xsubset(object),
  main = xpose.create.title.hist(x, object, subset, ...),
  xlb = NULL,
  ylb = "Density",
  hicol = object@Prefs@Graph.prefs$hicol,
  hilty = object@Prefs@Graph.prefs$hilty,
  hilwd = object@Prefs@Graph.prefs$hilwd,
  hidcol = object@Prefs@Graph.prefs$hidcol,
  hidlty = object@Prefs@Graph.prefs$hidlty,
  hidlwd = object@Prefs@Graph.prefs$hidlwd,
  hiborder = object@Prefs@Graph.prefs$hiborder,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)

Arguments

`x`	A string or a vector of strings with the name(s) of the x-variable(s).
`object`	An "xpose.data" object.
`inclZeroWRES`	A logical value indicating whether rows with WRES=0 should be plotted.
`onlyfirst`	A logical value indicating whether only the first row per individual should be included in the plot.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`type`	The type of histogram to make. See `histogram`.
`aspect`	The aspect ratio of the display (see `histogram`).
`scales`	A list to be used for the `scales` argument in `histogram`.
`by`	A string or a vector of strings with the name(s) of the conditioning variables.
`force.by.factor`	Logical value. If TRUE, and `by` is not `NULL`, the variable specified by `by` is taken as categorical.
`ordby`	A string with the name of a variable to be used to reorder any factor conditioning variables (`by`). The variable is used in a call to the `reorder.factor` function.
`byordfun`	The name of the function to be used when reordering a factor conditioning variable (see argument `ordby`)
`shingnum`	The number of shingles ("parts") a continuous conditioning variable should be divided into.
`shingol`	The amount of overlap between adjacent shingles (see argument `shingnum`)
`strip`	The name of the function to be used as the strip argument to the `xyplot`.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`main`	A string giving the plot title or `NULL` if none.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`hicol`	the fill colour of the histogram - an integer or string. The default is blue (see `histogram`).
`hilty`	the border line type of the histogram - an integer. The default is 1 (see `histogram`).
`hilwd`	the border line width of the histogram - an integer. The default is 1 (see `histogram`).
`hidcol`	the fill colour of the density line - an integer or string. The default is black (see `histogram`).
`hidlty`	the border line type of the density line - an integer. The default is 1 (see `histogram`).
`hidlwd`	the border line width of the density line - an integer. The default is 1 (see `histogram`).
`hiborder`	the border colour of the histogram - an integer or string. The default is black (see `histogram`).
`mirror`	Should we create mirror plots from simulation data? Value can be `FALSE`, `TRUE` or `1` for one mirror plot, or `3` for three mirror plots.
`max.plots.per.page`	The maximum number of plots per page that can be created with the mirror plots.
`mirror.aspect`	The aspect ratio of the plots used for mirror functionality.
`pass.plot.list`	Should we pass the list of plots created with mirror or should we print them directly. Values can be `TRUE/FALSE`.
`x.cex`	The size of the x-axis label.
`y.cex`	The size of the y-axis label.
`main.cex`	The size of the title.
`mirror.internal`	an internal mirror argument used in `create.mirror`. Checks if the `strip` argument from `xyplot` has been used.
`...`	Other arguments passed to `xpose.plot.histogram`.

Details

x can be either numeric or factor, and can be either single valued strings or vectors of strings.

Value

Returns a histogram.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpose.plot.histogram("AGE", xpdb5, onlyfirst = TRUE)
xpose.plot.histogram(c("SEX", "AGE"), xpdb5, onlyfirst = TRUE) 

## End(Not run)


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

xpose.plot.histogram("AGE", xpdb5, onlyfirst = TRUE)
xpose.plot.histogram(c("SEX", "AGE"), xpdb5, onlyfirst = TRUE) 

## End(Not run)

The generic Xpose functions for QQ plots

Description

This is a wrapper function for the lattice qqmath function.

Usage

xpose.plot.qq(
  x,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  aspect = object@[email protected]$aspect,
  scales = list(),
  by = object@[email protected]$condvar,
  force.by.factor = FALSE,
  ordby = object@[email protected]$ordby,
  byordfun = object@[email protected]$byordfun,
  shingnum = object@[email protected]$shingnum,
  shingol = object@[email protected]$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  subset = xsubset(object),
  main = xpose.create.title.hist(x, object, subset, ...),
  xlb = "Quantiles of Normal",
  ylb = paste("Quantiles of ", xlabel(x, object), sep = ""),
  pch = object@[email protected]$pch,
  col = object@[email protected]$col,
  cex = object@[email protected]$cex,
  abllty = object@[email protected]$abllty,
  abllwd = object@[email protected]$abllwd,
  ablcol = object@[email protected]$ablcol,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)
xpose.plot.qq(
  x,
  object,
  inclZeroWRES = FALSE,
  onlyfirst = FALSE,
  samp = NULL,
  aspect = object@Prefs@Graph.prefs$aspect,
  scales = list(),
  by = object@Prefs@Graph.prefs$condvar,
  force.by.factor = FALSE,
  ordby = object@Prefs@Graph.prefs$ordby,
  byordfun = object@Prefs@Graph.prefs$byordfun,
  shingnum = object@Prefs@Graph.prefs$shingnum,
  shingol = object@Prefs@Graph.prefs$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  subset = xsubset(object),
  main = xpose.create.title.hist(x, object, subset, ...),
  xlb = "Quantiles of Normal",
  ylb = paste("Quantiles of ", xlabel(x, object), sep = ""),
  pch = object@Prefs@Graph.prefs$pch,
  col = object@Prefs@Graph.prefs$col,
  cex = object@Prefs@Graph.prefs$cex,
  abllty = object@Prefs@Graph.prefs$abllty,
  abllwd = object@Prefs@Graph.prefs$abllwd,
  ablcol = object@Prefs@Graph.prefs$ablcol,
  mirror = FALSE,
  max.plots.per.page = 4,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)

Arguments

`x`	A string or a vector of strings with the name(s) of the x-variable(s).
`object`	An "xpose.data" object.
`inclZeroWRES`	A logical value indicating whether rows with WRES=0 should be plotted.
`onlyfirst`	A logical value indicating whether only the first row per individual should be included in the plot.
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`aspect`	The aspect ratio of the display (see `qqmath`).
`scales`	A list to be used for the `scales` argument in `qqmath`.
`by`	A string or a vector of strings with the name(s) of the conditioning variables.
`force.by.factor`	Logical value. If TRUE, and `by` is not `NULL`, the variable specified by `by` is taken as categorical.
`ordby`	A string with the name of a variable to be used to reorder any factor conditioning variables (`by`). The variable is used in a call to the `reorder` function.
`byordfun`	The name of the function to be used when reordering a factor conditioning variable (see argument `ordby`).
`shingnum`	The number of shingles ("parts") a continuous conditioning variable should be divided into.
`shingol`	The amount of overlap between adjacent shingles (see argument `shingnum`).
`strip`	The name of the function to be used as the strip argument to the `xyplot`.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`main`	A string giving the plot title or `NULL` if none.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`pch`	Plotting symbol.
`col`	Color of plotting symbol.
`cex`	Amount to scale the plotting character by.
`abllty`	Line type for qqline.
`abllwd`	Line width for qqline.
`ablcol`	Color for qqline.
`mirror`	Should we create mirror plots from simulation data? Value can be `FALSE`, `TRUE` or `1` for one mirror plot, or `3` for three mirror plots.
`max.plots.per.page`	The maximum number of plots per page that can be created with the mirror plots.
`mirror.aspect`	The aspect ratio of the plots used for mirror functionality.
`pass.plot.list`	Should we pass the list of plots created with mirror or should we print them directly. Values can be `TRUE/FALSE`.
`x.cex`	The size of the x-axis label.
`y.cex`	The size of the y-axis label.
`main.cex`	The size of the title.
`mirror.internal`	an internal mirror argument used in `create.mirror`. Checks if the `strip` argument from `qqmath` has been used.
`...`	Other arguments passed to `xpose.plot.qq`.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## A QQ plot of WRES
xpose.plot.qq("WRES", xpdb5) 

## End(Not run)


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## A QQ plot of WRES
xpose.plot.qq("WRES", xpdb5) 

## End(Not run)

The Xpose 4 generic functions for scatterplot matrices.

Description

This function is a wrapper for the lattice splom function.

Usage

xpose.plot.splom(
  plist,
  object,
  varnames = NULL,
  main = "Scatterplot Matrix",
  xlb = NULL,
  ylb = NULL,
  scales = list(),
  onlyfirst = TRUE,
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  by = object@[email protected]$condvar,
  force.by.factor = FALSE,
  include.cat.vars = FALSE,
  ordby = NULL,
  byordfun = object@[email protected]$byordfun,
  shingnum = object@[email protected]$shingnum,
  shingol = object@[email protected]$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  groups = NULL,
  ids = object@[email protected]$ids,
  smooth = TRUE,
  lmline = NULL,
  panel = xpose.panel.splom,
  aspect = object@[email protected]$aspect,
  samp = NULL,
  max.plots.per.page = 4,
  mirror = FALSE,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)
xpose.plot.splom(
  plist,
  object,
  varnames = NULL,
  main = "Scatterplot Matrix",
  xlb = NULL,
  ylb = NULL,
  scales = list(),
  onlyfirst = TRUE,
  inclZeroWRES = FALSE,
  subset = xsubset(object),
  by = object@Prefs@Graph.prefs$condvar,
  force.by.factor = FALSE,
  include.cat.vars = FALSE,
  ordby = NULL,
  byordfun = object@Prefs@Graph.prefs$byordfun,
  shingnum = object@Prefs@Graph.prefs$shingnum,
  shingol = object@Prefs@Graph.prefs$shingol,
  strip = function(...) strip.default(..., strip.names = c(TRUE, TRUE)),
  groups = NULL,
  ids = object@Prefs@Graph.prefs$ids,
  smooth = TRUE,
  lmline = NULL,
  panel = xpose.panel.splom,
  aspect = object@Prefs@Graph.prefs$aspect,
  samp = NULL,
  max.plots.per.page = 4,
  mirror = FALSE,
  mirror.aspect = "fill",
  pass.plot.list = FALSE,
  x.cex = NULL,
  y.cex = NULL,
  main.cex = NULL,
  mirror.internal = list(strip.missing = missing(strip)),
  ...
)

Arguments

`plist`	A vector of strings containing variable names for the scatterplot matrix.
`object`	An "xpose.data" object.
`varnames`	A vector of strings containing labels for the variables in the scatterplot matrix.
`main`	A string giving the plot title or `NULL` if none.
`xlb`	A string giving the label for the x-axis. `NULL` if none.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`scales`	A list to be used for the `scales` argument in `xyplot`.
`onlyfirst`	A logical value indicating whether only the first row per individual should be included in the plot.
`inclZeroWRES`	A logical value indicating whether rows with WRES=0 should be plotted.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`by`	A string or a vector of strings with the name(s) of the conditioning variables.
`force.by.factor`	Logical value. If TRUE, and `by` is not `NULL`, the variable specified by `by` is taken as categorical.
`include.cat.vars`	Logical value.
`ordby`	A string with the name of a variable to be used to reorder any factor conditioning variables (`by`). The variable is used in a call to the `reorder.factor` function.
`byordfun`	The name of the function to be used when reordering a factor conditioning variable (see argument `ordby`)
`shingnum`	The number of shingles ("parts") a continuous conditioning variable should be divided into.
`shingol`	The amount of overlap between adjacent shingles (see argument `shingnum`)
`strip`	The name of the function to be used as the strip argument to the `xyplot`.
`groups`	A string with the name of any grouping variable (used as the groups argument to `panel.xyplot`.
`ids`	A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` xpose data variable).
`smooth`	A `NULL` value indicates that no superposed line should be added to the graph. If `TRUE` then a smooth of the data will be superimposed.
`lmline`	logical variable specifying whether a linear regression line should be superimposed over an `xyplot`. `NULL` ~ FALSE. (`y~x`)
`panel`	The name of the panel function to use.
`aspect`	The aspect ratio of the display (see `xyplot`).
`samp`	An integer between 1 and object@Nsim (see`xpose.data-class`) specifying which of the simulated data sets to extract from SData.
`max.plots.per.page`	The maximum number of plots per page that can be created with the mirror plots.
`mirror`	Should we create mirror plots from simulation data? Value can be `FALSE`, `TRUE` or `1` for one mirror plot, or `3` for three mirror plots.
`mirror.aspect`	The aspect ratio of the plots used for mirror functionality.
`pass.plot.list`	Should we pass the list of plots created with mirror or should we print them directly. Values can be `TRUE/FALSE`.
`x.cex`	The size of the x-axis label.
`y.cex`	The size of the y-axis label.
`main.cex`	The size of the title.
`mirror.internal`	an internal mirror argument used in `create.mirror`. Checks if the `strip` argument from `qqmath` has been used.
`...`	Other arguments passed to `xpose.panel.default`.

Details

If ids is TRUE, text labels are added to the plotting symbols. The labels are taken from the idlab xpose data variable. The way the text labels are plotted is governed by the idsmode argument (passed down to the panel function). idsmode=NULL (the default) means that only extreme data points are labelled while a non-NULL value adds labels to all data points (the default in Xpose 3). xpose.panel.default identifies extreme data points by fitting a loess smooth (y~x) and looking at the residuals from that fit. Points that are associated with the highest/lowest residuals are labelled. "High" and "low" are judged by the panel function parameter idsext, which gives the fraction of the total number of data points that are to be judged extreme in the "up" and "down" direction. The default value for idsext is 0.05 (see link{xpose.prefs-class}). There is also a possibility to label only the high or low extreme points. This is done through the idsdir argument to xpose.panel.default. A value of "both" (the default) means that both high and low extreme points are labelled while "up" and "down" labels the high and low extreme points respectively.

More graphical parameters may be passed to xpose.panel.splom. for example, if you want to adjust the size of the varnames and axis tick labels you can use the parameters varname.cex=0.5 and axis.text.cex=0.5.

Value

Returns a scatterplot matrix graph object.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

Examples


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## CL, WT, HT, SEX with a regression line
xpose.plot.splom(c("CL", "WT", "HT", "SEX"), xpdb5, lmline = TRUE) 

## End(Not run)


## Not run: 
## xpdb5 is an Xpose data object
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## CL, WT, HT, SEX with a regression line
xpose.plot.splom(c("CL", "WT", "HT", "SEX"), xpdb5, lmline = TRUE) 

## End(Not run)

Class "xpose.prefs"

Description

An object of the "xpose.prefs" class holds information about all the variable and graphical preferences for a particular "xpose.data" object.

Objects from the Class

Objects can be created by calls of the form new("xpose.prefs",...) but this is usually not necessary since the "xpose.prefs" object is created at the same time as the "xpose.data" object.

Author(s)

Niclas Jonsson & Andrew Hooker

Summarize an xpose database

Description

Summarize an xpose database

Usage

xpose.print(object, long = TRUE)
xpose.print(object, long = TRUE)

Arguments

`object`	An xpose data object
`long`	long format or not.

Value

Examples

xpose.print(simpraz.xpdb)
xpose.print(simpraz.xpdb)

Print a pretty string.

Description

Print a string with a certain number of characters per row.

Usage

xpose.string.print(value, fill = 60, file = "")
xpose.string.print(value, fill = 60, file = "")

Arguments

`value`	The text to print.
`fill`	How wide should the text be per row.
`file`	Where to print. `""` means to the screen.

Author(s)

Niclas Jonsson and Andrew C. Hooker

Visual Predictive Check (VPC) using XPOSE

Description

This Function is used to create a VPC in xpose using the output from the vpc command in Pearl Speaks NONMEM (PsN). The function reads in the output files created by PsN and creates a plot from the data. The dependent variable, independent variable and conditioning variable are automatically determined from the PsN files.

Usage

xpose.VPC(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  ids = FALSE,
  type = "p",
  by = NULL,
  PI = NULL,
  PI.ci = "area",
  PI.ci.area.smooth = FALSE,
  PI.real = TRUE,
  subset = NULL,
  main = "Default",
  main.sub = NULL,
  main.sub.cex = 0.85,
  inclZeroWRES = FALSE,
  force.x.continuous = FALSE,
  funy = NULL,
  logy = FALSE,
  ylb = "Default",
  verbose = FALSE,
  PI.x.median = TRUE,
  PI.rug = "Default",
  PI.identify.outliers = TRUE,
  ...
)
xpose.VPC(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  ids = FALSE,
  type = "p",
  by = NULL,
  PI = NULL,
  PI.ci = "area",
  PI.ci.area.smooth = FALSE,
  PI.real = TRUE,
  subset = NULL,
  main = "Default",
  main.sub = NULL,
  main.sub.cex = 0.85,
  inclZeroWRES = FALSE,
  force.x.continuous = FALSE,
  funy = NULL,
  logy = FALSE,
  ylb = "Default",
  verbose = FALSE,
  PI.x.median = TRUE,
  PI.rug = "Default",
  PI.identify.outliers = TRUE,
  ...
)

Arguments

`vpc.info`	The results file from the `vpc` command in PsN. for example ‘vpc_results.csv’, or if the file is in a separate directory ‘./vpc_dir1/vpc_results.csv’.
`vpctab`	The ‘vpctab’ from the `vpc` command in PsN. For example ‘vpctab5’, or if the file is in a separate directory ‘./vpc_dir1/vpctab5’. Can be `NULL`. The default looks in the current working directory and takes the first file that starts with ‘vpctab’ that it finds. Note that this default can result in the wrong files being read if there are multiple ‘vpctab’ files in the directory. One of `object` or `vpctab` is required. If both are present then the information from the `vpctab` will over-ride the xpose data object `object` (i.e. the values from the vpctab will replace any matching values in the `object\@Data` portion of the xpose data object).
`object`	An xpose data object. Created from `xpose.data`. One of `object` or `vpctab` is required. If both are present then the information from the `vpctab` will over-ride the xpose data object `object` (i.e. the values from the vpctab will replace any matching values in the `object\@Data` portion of the xpose data object).
`ids`	A logical value indicating whether text ID labels should be used as plotting symbols (the variable used for these symbols indicated by the `idlab` xpose data variable). Can be `FALSE` or `TRUE`.
`type`	Character string describing the way the points in the plot will be displayed. For more details, see `plot`. Use `type="n"` if you don't want observations in the plot.
`by`	A string or a vector of strings with the name(s) of the conditioning variables. For example `by = c("SEX","WT")`. Because the function automatically determines the conditioning variable from the PsN input file specified in `vpc.info`, the `by` command can control if separate plots are created for each condition (`by=NULL`), or if a conditioning plot should be created (`by="WT"` for example). If the `vpc.info` file has a conditioning variable then `by` must match that variable. If there is no conditioning variable in `vpc.info` then the `PI` for each conditioned plot will be the `PI` for the entire data set (not only for the conditioning subset).
`PI`	Either "lines", "area" or "both" specifying whether prediction intervals (as lines, a shaded area or both) should be added to the plot. `NULL` means no prediction interval.
`PI.ci`	Plot the confidence interval for the simulated data's percentiles for each bin (for each simulated data set compute the percentiles for each bin, then, from all of the percentiles from all of the simulated datasets compute the 95% CI of these percentiles). Values can be `"both"`, `"area"` or `"lines"`. These CIs can be used to asses the `PI.real` values for model misspecification. Note that with few observations per bin the CIs will be approximate because the percentiles in each bin will be approximate. For example, the 95th percentile of 4 data points will always be the largest of the 4 data points.
`PI.ci.area.smooth`	Should the "area" for `PI.ci` be smoothed to match the "lines" argument? Allowed values are `TRUE/FALSE`. The "area" is set by default to show the bins used in the `PI.ci` computation. By smoothing, information is lost and, in general, the confidence intervals will be smaller than they are in reality.
`PI.real`	Plot the percentiles of the real data in the various bins. values can be NULL or TRUE. Note that for a bin with few actual observations the percentiles will be approximate. For example, the 95th percentile of 4 data points will always be the largest of the 4 data points.
`subset`	A string giving the subset expression to be applied to the data before plotting. See `xsubset`.
`main`	A string giving the plot title or `NULL` if none. `"Default"` creates a default title.
`main.sub`	Used for names above each plot when using multiple plots. Should be a vector `c("Group 1","Group 2")`
`main.sub.cex`	The size of the `main.sub` titles.
`inclZeroWRES`	Logical value indicating whether rows with WRES=0 is included in the plot.
`force.x.continuous`	Logical value indicating whether x-values should be converted to continuous variables, even if they are defined as factors.
`funy`	String of function to apply to Y data. For example "abs"
`logy`	Logical value indicating whether the y-axis should be logarithmic, base 10.
`ylb`	Label for the y-axis
`verbose`	Should warning messages and other diagnostic information be passed to screen? (TRUE or FALSE)
`PI.x.median`	Should the x-location of percentile lines in a bin be marked at the median of the x-values? (`TRUE` or `FALSE`)
`PI.rug`	Should there be markings on the plot showing where the binning intervals for the VPC are (or the locations of the independent variable used for each VPC calculation if binning is not used)?
`PI.identify.outliers`	Should outlying percentiles of the real data be highlighted? (TRUE of FALSE)
`...`	Other arguments passed to `xpose.panel.default`, `xpose.plot.default` and others. Please see these functions for more descriptions of what you can do.

Value

A plot or a list of plots.

Additional arguments

Below are some of the additional arguments that can control the look and feel of the VPC. See xpose.panel.default for all potential options.

Additional graphical elements available in the VPC plots.

PI.mirror = NULL, TRUE or AN.INTEGER.VALUE: Plot the percentiles of one simulated data set in each bin. TRUE takes the first mirror from ‘vpc_results.csv’ and AN.INTEGER.VALUE can be 1, 2, ...{} n where n is the number of mirror's output in the ‘vpc_results.csv’ file.
PI.limits = c(0.025, 0.975): A vector of two values that describe the limits of the prediction interval that should be displayed. These limits should be found in the ‘vpc_results.csv’ file. These limits are also used as the percentages for the PI.real, PI.mirror and PI.ci. However, the confidence interval in PI.ci is always the one defined in the ‘vpc_results.csv’ file.

Additional options to control the look and feel of the PI. See See grid.polygon and plot for more details.

PI.arcol: The color of the PI area
PI.up.lty: The upper line type. can be "dotted" or "dashed", etc.
PI.up.type: The upper type used for plotting. Defaults to a line.
PI.up.col: The upper line color
PI.up.lwd: The upper line width
PI.down.lty: The lower line type. can be "dotted" or "dashed", etc.
PI.down.type: The lower type used for plotting. Defaults to a line.
PI.down.col: The lower line color
PI.down.lwd: The lower line width
PI.med.lty: The median line type. can be "dotted" or "dashed", etc.
PI.med.type: The median type used for plotting. Defaults to a line.
PI.med.col: The median line color
PI.med.lwd: The median line width

Additional options to control the look and feel of the PI.ci. See See grid.polygon and plot for more details.

PI.ci.up.arcol: The color of the upper PI.ci.
PI.ci.med.arcol: The color of the median PI.ci.
PI.ci.down.arcol: The color of the lower PI.ci.
PI.ci.up.lty: The upper line type. can be "dotted" or "dashed", etc.
PI.ci.up.type: The upper type used for plotting. Defaults to a line.
PI.ci.up.col: The upper line color
PI.ci.up.lwd: The upper line width
PI.ci.down.lty: The lower line type. can be "dotted" or "dashed", etc.
PI.ci.down.type: The lower type used for plotting. Defaults to a line.
PI.ci.down.col: The lower line color
PI.ci.down.lwd: The lower line width
PI.ci.med.lty: The median line type. can be "dotted" or "dashed", etc.
PI.ci.med.type: The median type used for plotting. Defaults to a line.
PI.ci.med.col: The median line color
PI.ci.med.lwd: The median line width
PI.ci.area.smooth: Should the "area" for PI.ci be smoothed to match the "lines" argument? Allowed values are TRUE/FALSE. The "area" is set by default to show the bins used in the PI.ci computation. By smoothing, information is lost and, in general, the confidence intervals will be smaller than they are in reality.

Additional options to control the look and feel of the PI.real. See See grid.polygon and plot for more details.

PI.real.up.lty: The upper line type. can be "dotted" or "dashed", etc.
PI.real.up.type: The upper type used for plotting. Defaults to a line.
PI.real.up.col: The upper line color
PI.real.up.lwd: The upper line width
PI.real.down.lty: The lower line type. can be "dotted" or "dashed", etc.
PI.real.down.type: The lower type used for plotting. Defaults to a line.
PI.real.down.col: The lower line color
PI.real.down.lwd: The lower line width
PI.real.med.lty: The median line type. can be "dotted" or "dashed", etc.
PI.real.med.type: The median type used for plotting. Defaults to a line.
PI.real.med.col: The median line color
PI.real.med.lwd: The median line width

Additional options to control the look and feel of the PI.mirror. See See plot for more details.

PI.mirror.up.lty: The upper line type. can be "dotted" or "dashed", etc.
PI.mirror.up.type: The upper type used for plotting. Defaults to a line.
PI.mirror.up.col: The upper line color
PI.mirror.up.lwd: The upper line width
PI.mirror.down.lty: The lower line type. can be "dotted" or "dashed", etc.
PI.mirror.down.type: The lower type used for plotting. Defaults to a line.
PI.mirror.down.col: The lower line color
PI.mirror.down.lwd: The lower line width
PI.mirror.med.lty: The median line type. can be "dotted" or "dashed", etc.
PI.mirror.med.type: The median type used for plotting. Defaults to a line.
PI.mirror.med.col: The median line color
PI.mirror.med.lwd: The median line width

Author(s)

Andrew Hooker

Examples


## Not run: 
library(xpose4)

xpose.VPC()

## to be more clear about which files should be read in
vpc.file <- "vpc_results.csv"
vpctab <- "vpctab5"
xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)

## with lines and a shaded area for the prediction intervals
xpose.VPC(vpc.file,vpctab=vpctab,PI="both")

## with the percentages of the real data
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T)

## with mirrors (if supplied in 'vpc.file')
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.mirror=5)

## with CIs
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci="area")
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci="area",PI=NULL)

## stratification (if 'vpc.file' is stratified)
cond.var <- "WT"
xpose.VPC(vpc.file,vpctab=vpctab)
xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var)
xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both",by=cond.var,type="n")

## with no data points in the plot
xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var,PI.real=T,PI.ci="area",PI=NULL,type="n")

## with different DV and IDV, just read in new files and plot
vpc.file <- "vpc_results.csv"
vpctab <- "vpctab5"
cond.var <- "WT"
xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both",by=cond.var)
xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both")

## to use an xpose data object instead of vpctab
##
## In this example
## we expect to find the required NONMEM run and table files for run
## 5 in the current working directory
runnumber <- 5
xpdb <- xpose.data(runnumber)
xpose.VPC(vpc.file,object=xpdb)

## to read files in a directory different than the current working directory 
vpc.file <- "./vpc_strat_WT_4_mirror_5/vpc_results.csv"
vpctab <- "./vpc_strat_WT_4_mirror_5/vpctab5"
xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)

## to rearrange order of factors in VPC plot
xpdb@Data$SEX <- factor(xpdb@Data$SEX,levels=c("2","1"))
xpose.VPC(by="SEX",object=xpdb)


## End(Not run)


## Not run: 
library(xpose4)

xpose.VPC()

## to be more clear about which files should be read in
vpc.file <- "vpc_results.csv"
vpctab <- "vpctab5"
xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)

## with lines and a shaded area for the prediction intervals
xpose.VPC(vpc.file,vpctab=vpctab,PI="both")

## with the percentages of the real data
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T)

## with mirrors (if supplied in 'vpc.file')
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.mirror=5)

## with CIs
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci="area")
xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci="area",PI=NULL)

## stratification (if 'vpc.file' is stratified)
cond.var <- "WT"
xpose.VPC(vpc.file,vpctab=vpctab)
xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var)
xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both",by=cond.var,type="n")

## with no data points in the plot
xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var,PI.real=T,PI.ci="area",PI=NULL,type="n")

## with different DV and IDV, just read in new files and plot
vpc.file <- "vpc_results.csv"
vpctab <- "vpctab5"
cond.var <- "WT"
xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both",by=cond.var)
xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both")

## to use an xpose data object instead of vpctab
##
## In this example
## we expect to find the required NONMEM run and table files for run
## 5 in the current working directory
runnumber <- 5
xpdb <- xpose.data(runnumber)
xpose.VPC(vpc.file,object=xpdb)

## to read files in a directory different than the current working directory 
vpc.file <- "./vpc_strat_WT_4_mirror_5/vpc_results.csv"
vpctab <- "./vpc_strat_WT_4_mirror_5/vpctab5"
xpose.VPC(vpc.info=vpc.file,vpctab=vpctab)

## to rearrange order of factors in VPC plot
xpdb@Data$SEX <- factor(xpdb@Data$SEX,levels=c("2","1"))
xpose.VPC(by="SEX",object=xpdb)


## End(Not run)

Xpose Visual Predictive Check (VPC) for both continuous and Limit of Quantification data.

Description

Xpose Visual Predictive Check (VPC) for both continuous and Below or Above Limit of Quantification (BLQ or ALQ) data.

Usage

xpose.VPC.both(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  subset = NULL,
  main = "Default",
  main.sub = NULL,
  inclZeroWRES = FALSE,
  cont.logy = F,
  hline = "default",
  add.args.cont = list(),
  add.args.cat = list(),
  ...
)
xpose.VPC.both(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  subset = NULL,
  main = "Default",
  main.sub = NULL,
  inclZeroWRES = FALSE,
  cont.logy = F,
  hline = "default",
  add.args.cont = list(),
  add.args.cat = list(),
  ...
)

Arguments

`vpc.info`	Name of PSN file to use. File will come from `VPC` command in PsN.
`vpctab`	Name of vpctab file produced from PsN.
`object`	Xpose data object.
`subset`	Subset of data to look at.
`main`	Title for plot.
`main.sub`	Used for names above each plot when using multiple plots. Should be a vector, e.g. `c("title 1","title 2")`.
`inclZeroWRES`	Include WRES=0 rows in the computations for these plots?
`cont.logy`	Should the continuous plot y-axis be on the log scale?
`hline`	Horizontal line marking the limits of quantification. If they are defined, they must be a vector of values.
`add.args.cont`	Additional arguments to the continuous plot. `xpose.VPC`.
`add.args.cat`	Additional arguments to the categorical plot. `xpose.VPC.categorical`.
`...`	Additional arguments to both plots.

Author(s)

Andrew C. Hooker

Examples


## Not run: 
library(xpose4)

## move to the directory where results from PsN
## are found
cur.dir <- getwd()
setwd(paste(cur.dir,"/vpc_cont_LLOQ/",sep=""))

xpose.VPC()
xpose.VPC.categorical(censored=T)

xpose.VPC.both()

xpose.VPC.both(subset="DV>1.75")

xpose.VPC.both(add.args.cont=list(ylim=c(0,80)))

xpose.VPC.both(add.args.cont = list(ylim = c(0.01, 80)), xlim = c(0, 
    40), add.args.cat = list(ylim = c(0, 0.4)), cont.logy = T)

xpose.VPC.both(cont.logy=T)

## End(Not run)

## Not run: 
library(xpose4)

## move to the directory where results from PsN
## are found
cur.dir <- getwd()
setwd(paste(cur.dir,"/vpc_cont_LLOQ/",sep=""))

xpose.VPC()
xpose.VPC.categorical(censored=T)

xpose.VPC.both()

xpose.VPC.both(subset="DV>1.75")

xpose.VPC.both(add.args.cont=list(ylim=c(0,80)))

xpose.VPC.both(add.args.cont = list(ylim = c(0.01, 80)), xlim = c(0, 
    40), add.args.cat = list(ylim = c(0, 0.4)), cont.logy = T)

xpose.VPC.both(cont.logy=T)

## End(Not run)

Xpose visual predictive check for categorical data.

Description

Xpose visual predictive check for categorical data (binary, ordered categorical and count data).

Usage

xpose.VPC.categorical(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  subset = NULL,
  main = "Default",
  main.sub = "Default",
  main.sub.cex = 0.85,
  real.col = 4,
  real.lty = "b",
  real.cex = 1,
  real.lwd = 1,
  median.line = FALSE,
  median.col = "darkgrey",
  median.lty = 1,
  ci.lines = FALSE,
  ci.col = "blue",
  ci.lines.col = "darkblue",
  ci.lines.lty = 3,
  xlb = "Default",
  ylb = "Proportion of Total",
  force.x.continuous = FALSE,
  level.to.plot = NULL,
  max.plots.per.page = 1,
  rug = TRUE,
  rug.col = "orange",
  censored = FALSE,
  ...
)
xpose.VPC.categorical(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  subset = NULL,
  main = "Default",
  main.sub = "Default",
  main.sub.cex = 0.85,
  real.col = 4,
  real.lty = "b",
  real.cex = 1,
  real.lwd = 1,
  median.line = FALSE,
  median.col = "darkgrey",
  median.lty = 1,
  ci.lines = FALSE,
  ci.col = "blue",
  ci.lines.col = "darkblue",
  ci.lines.lty = 3,
  xlb = "Default",
  ylb = "Proportion of Total",
  force.x.continuous = FALSE,
  level.to.plot = NULL,
  max.plots.per.page = 1,
  rug = TRUE,
  rug.col = "orange",
  censored = FALSE,
  ...
)

Arguments

`vpc.info`	Name of PSN file to use. File will come from `VPC` command in PsN.
`vpctab`	Name of vpctab file produced from PsN.
`object`	Xpose data object.
`subset`	Subset of data to look at.
`main`	Title for plot.
`main.sub`	Used for names above each plot when using multiple plots. Should be a vector, e.g. `c("title 1","title 2")`.
`main.sub.cex`	Size of `main.sub`
`real.col`	Color of real line.
`real.lty`	Real line type.
`real.cex`	Size of real line.
`real.lwd`	Width of real line.
`median.line`	Dray a median line?
`median.col`	Color of median line.
`median.lty`	median line type.
`ci.lines`	Lines marking confidence interval?
`ci.col`	Color of CI area.
`ci.lines.col`	Color of CI lines.
`ci.lines.lty`	Type of CI lines.
`xlb`	X-axis label. If other than "default"" passed directly to `xyplot`.
`ylb`	Y-axis label. Passed directly to `xyplot`.
`force.x.continuous`	For the x variable to be continuous.
`level.to.plot`	Which levels of the variable to plot. Smallest level is 1, largest is number_of_levels. For example, with 4 levels, the largest level would be 4, the smallest would be 1.
`max.plots.per.page`	The number of plots per page.
`rug`	Should there be markings on the plot showing where the intervals for the VPC are?
`rug.col`	Color of the rug.
`censored`	Is this censored data? Censored data can be both below and above the limit of quantification.
`...`	Additional information passed to function.

Author(s)

Andrew C. Hooker

Examples


## Not run: 
library(xpose4)

## move to the directory where results from PsN
## are found
cur.dir <- getwd()
setwd(paste(cur.dir,"/binary/vpc_36",sep=""))

xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4)
xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4,by="DOSE")

## ordered categorical plots
setwd(paste(cur.dir,"/ordered_cat/vpc_45",sep=""))
xpose.VPC.categorical()


## count
setwd(paste(cur.dir,"/count/vpc65b",sep=""))
xpose.VPC.categorical()

setwd(paste(cur.dir,"/count/vpc65a",sep=""))
xpose.VPC.categorical()


## End(Not run)

## Not run: 
library(xpose4)

## move to the directory where results from PsN
## are found
cur.dir <- getwd()
setwd(paste(cur.dir,"/binary/vpc_36",sep=""))

xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4)
xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4,by="DOSE")

## ordered categorical plots
setwd(paste(cur.dir,"/ordered_cat/vpc_45",sep=""))
xpose.VPC.categorical()


## count
setwd(paste(cur.dir,"/count/vpc65b",sep=""))
xpose.VPC.categorical()

setwd(paste(cur.dir,"/count/vpc65a",sep=""))
xpose.VPC.categorical()


## End(Not run)

Classic menu system for Xpose 4

Description

Classic menu system for Xpose 4

Usage

xpose4()
xpose4()

Author(s)

Andrew Hooker

Examples


## Not run: 
xpose4()

## End(Not run)

## Not run: 
xpose4()

## End(Not run)

Extract or set the value of the Subset slot.

Description

Extract or set the value of the Subset slot of an "xpose.data" object.

Usage

xsubset(object)

xsubset(object) <- value
xsubset(object)

xsubset(object) <- value

Arguments

`object`	An "xpose.data" object.
`value`	A string with the subset expression.

Details

The subset string has the same syntax as the subset argument to, e.g. panel.xyplot. Note, however, that the "xpose.data" subset is not used as an argument to panel.xyplot. It is intended as the subset argument to the Data and SData functions.

Value

A string representing the subset expression.

Functions

xsubset(object) <- value: assign value with a string representing the subset expression

Author(s)

Niclas Jonsson

Examples

xpdb <- simpraz.xpdb
xsubset(xpdb) <- "DV > 0"
xsubset(xpdb)

xpdb <- simpraz.xpdb
xsubset(xpdb) <- "DV > 0"
xsubset(xpdb)

Extract and set Xpose variable definitions.

Description

This function extracts and set Xpose variable definitions in "xpose.data" objects.

Usage

xvardef(x, object)

xvardef(object) <- value
xvardef(x, object)

xvardef(object) <- value

Arguments

`x`	The name of an xpose variable (see below).
`object`	An `xpose.data` object.
`value`	A two element vector of which the first element is the name of the variable and the second the column name in the Data slot of the object.

Details

The Xpose variable definitions are used to map particular variable types to column names in the data.frame in the Data slot of the "xpose.data" object. The single-valued Xpose variable definitions are: id, idlab, idv, occ, dv, pred, ipred, iwres, res. The (potentially) vector-valued Xpose variable definitions are: parms, covariates, ranpar, tvparms (parameters, covariates, random effects parameters=etas, typical value parameters). The default values of these can be found in the createXposeClasses function.

Value

Returns a string with the name of the data variable defined as the Xpose data variable.

Functions

xvardef(object) <- value: reset the which column the label dv points to in the Data slot of the xpose database object

Author(s)

Niclas Jonsson

Examples

xpdb <- simpraz.xpdb

## get the column name in the Data slot of object xpdb
## corresponding to the label dv
xvardef("dv", xpdb)

## reset the which column the label dv points to in the Data slot of
## object xpdb
xvardef(xpdb) <- c("dv", "DVA")

xpdb <- simpraz.xpdb

## get the column name in the Data slot of object xpdb
## corresponding to the label dv
xvardef("dv", xpdb)

## reset the which column the label dv points to in the Data slot of
## object xpdb
xvardef(xpdb) <- c("dv", "DVA")

Package 'xpose4'

Help Index

The Xpose Package

Description

Details

How to make NONMEM generate input to Xpose

Author(s)

References

See Also

Examples

Model comparison plots, of absolute differences in goodness-of-fit predictors against covariates, for Xpose 4

Description

Usage

Arguments

Details

Value

Functions

Author(s)

See Also

Examples

Absolute conditional weighted residuals vs covariates for Xpose 4

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Absolute population conditional weighted residuals vs population predictions for Xpose 4

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Absolute value of the conditional weighted residuals vs. population predictions, conditioned on covariates, for Xpose 4

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Absolute population weighted residuals vs population predictions, and absolute individual weighted residuals vs individual predictions, for Xpose 4

Description

Usage

Arguments

Details

Value

Functions

Author(s)

See Also

Examples

box and whisker plots of the absolute value of the individual weighted residuals vs. covariates

Description

Usage

Arguments

Value

See Also

absolute value of the individual weighted residuals vs. the independent variable

Description

Usage

Arguments

Value

See Also

Absolute individual weighted residuals vs individual predictions for Xpose 4

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Absolute individual weighted residuals vs individual predictions, conditioned on covariates, for Xpose 4

Description