xpose4 (version 4.7.1)

xpose.VPC: 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,
  ...
)

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

See Also

read.vpctab read.npc.vpc.results xpose.panel.default xpose.plot.default

Other PsN functions: boot.hist(), bootscm.import(), npc.coverage(), randtest.hist(), read.npc.vpc.results(), read.vpctab(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Other specific functions: absval.cwres.vs.cov.bw(), absval.cwres.vs.pred.by.cov(), absval.cwres.vs.pred(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.ipred(), absval.iwres.vs.pred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred.by.cov(), absval.wres.vs.pred(), absval_delta_vs_cov_model_comp, addit.gof(), autocorr.cwres(), autocorr.iwres(), autocorr.wres(), basic.gof(), basic.model.comp(), cat.dv.vs.idv.sb(), cat.pc(), cov.splom(), cwres.dist.hist(), cwres.dist.qq(), cwres.vs.cov(), cwres.vs.idv.bw(), cwres.vs.idv(), cwres.vs.pred.bw(), cwres.vs.pred(), cwres.wres.vs.idv(), cwres.wres.vs.pred(), dOFV.vs.cov(), dOFV.vs.id(), dOFV1.vs.dOFV2(), data.checkout(), dv.preds.vs.idv(), dv.vs.idv(), dv.vs.ipred.by.cov(), dv.vs.ipred.by.idv(), dv.vs.ipred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), dv.vs.pred(), gof(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), ind.plots(), ipred.vs.idv(), iwres.dist.hist(), iwres.dist.qq(), iwres.vs.idv(), kaplan.plot(), par_cov_hist, par_cov_qq, parm.vs.cov(), parm.vs.parm(), pred.vs.idv(), ranpar.vs.cov(), runsum(), wres.dist.hist(), wres.dist.qq(), wres.vs.idv.bw(), wres.vs.idv(), wres.vs.pred.bw(), wres.vs.pred(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Examples

# 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)

# }
# NOT RUN {

# }