autocorr.cwres: Autocorrelation of conditional weighted residuals for Xpose 4

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Autocorrelation of conditional weighted residuals for Xpose 4

Description

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}.

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.

Value

Returns an autocorrelation plot for conditional weighted population residuals (CWRES).

Author(s)

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

See Also

xyplot, xpose.prefs-class, compute.cwres, xpose.data-class

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

Examples

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## 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)

xpose4 documentation built on May 29, 2024, 7:56 a.m.