wres.vs.pred: Population weighted residuals (WRES) plotted against...

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Population weighted residuals (WRES) plotted against population predictions (PRED) for Xpose 4

Description

This is a plot of population weighted residuals (WRES) vs population predictions (PRED), 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

wres.vs.pred(object, smooth = TRUE, abline = c(0, 0), ...)

Arguments

object	An xpose.data object.
smooth	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
abline	Vector of arguments to the panel.abline function. No abline is drawn if NULL.
...	Other arguments passed to link{xpose.plot.default}.

Details

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

Value

Returns an xyplot of WRES vs PRED.

Author(s)

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

See Also

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

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

Examples

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

wres.vs.pred(xpdb)

## A conditioning plot
wres.vs.pred(xpdb, by="HCTZ")

xpose4 documentation built on May 31, 2022, 5:07 p.m.