Random parameters plotted against covariates, for Xpose 4

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",
  ...
)

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}.

Value

Returns a stack of xyplots and histograms of random parameters against covariates.

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.

Other specific functions: absval.cwres.vs.cov.bw(), absval.cwres.vs.pred(), absval.cwres.vs.pred.by.cov(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.pred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred(), absval.wres.vs.pred.by.cov(), 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(), cwres.vs.idv.bw(), cwres.vs.pred(), cwres.vs.pred.bw(), 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(), dv.vs.ipred.by.cov(), dv.vs.ipred.by.idv(), dv.vs.pred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), gof(), ind.plots(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), 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(), runsum(), wres.dist.hist(), wres.dist.qq(), wres.vs.idv(), wres.vs.idv.bw(), wres.vs.pred(), wres.vs.pred.bw(), xpose.VPC(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Author

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

Examples


if (FALSE) { # \dontrun{
## 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)
} # }