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.

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)
} # }