cat.pc: Categorical (visual) predictive check.

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

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",
  ...
)

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

See Also

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(), 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(), xpose.VPC(), xpose4-package

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)

xpose4 documentation built on May 31, 2022, 5:07 p.m.