Autocorrelation of conditional weighted residuals for Xpose 4

This is an autocorrelation plot of conditional weighted residuals, a specific function in Xpose 4. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

autocorr.cwres(
  object,
  type = "p",
  smooth = TRUE,
  ids = F,
  main = "Default",
  ...
)

Arguments

object: An xpose.data object.
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.
smooth: Logical value indicating whether a smooth should be superimposed.
ids: A logical value indicating whether text labels should be used as plotting symbols (the variable used for these symbols indicated by the idlab xpose data variable).
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 an autocorrelation plot for conditional weighted population residuals (CWRES).

Details

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres 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.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(), 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


if (FALSE) { # \dontrun{
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)
} # }

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

## A vanilla plot
autocorr.cwres(xpdb)


## A conditioning plot
autocorr.cwres(xpdb, dilution=TRUE)


## Custom heading and axis labels
autocorr.cwres(xpdb, main="My conditioning plot", ylb="|CWRES|", xlb="PRED")


## Custom colours and symbols, IDs
autocorr.cwres(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)