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

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",
  ...
)

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}.

Value

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

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.

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.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(), ranpar.vs.cov(), 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

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

cwres.vs.cov(xpdb)