cwres.vs.pred.bw: Box-and-whisker plot of conditional weighted residuals vs...

In UUPharmacometrics/xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Box-and-whisker plot of conditional weighted residuals vs population predictions for Xpose 4

Description

This creates a box and whisker plot of conditional weighted residuals (CWRES) vs population predictions (PRED), and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

cwres.vs.pred.bw(object, ...)

Arguments

object	An xpose.data object.
...	Other arguments passed to link{xpose.plot.bw}.

Details

This creates a box and whisker plot of conditional weighted residuals (CWRES) vs population predictions (PRED), and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.bw function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

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

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

Value

Returns a box-and-whisker plot of CWRES vs PRED.

Author(s)

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

See Also

xpose.plot.bw, xpose.panel.bw, bwplot, 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.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.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

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

cwres.vs.pred.bw(xpdb)

UUPharmacometrics/xpose4 documentation built on Feb. 22, 2024, 5:02 p.m.