absval.cwres.vs.cov.bw: Absolute conditional weighted residuals vs covariates for...

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Absolute conditional weighted residuals vs covariates for Xpose 4

Description

This creates a stack of box and whisker plot of absolute population conditional weighted residuals (|CWRES|) vs covariates, and is a specific function in Xpose 4. It is a wrapper encapsulating arguments to the codexpose.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

absval.cwres.vs.cov.bw(object, xlb = "|CWRES|", main = "Default", ...)

Arguments

object	An xpose.data object.
xlb	A string giving the label for the x-axis. NULL if none.
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 xpose.plot.bw.

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 box-and-whisker plots are available. See xpose.plot.bw for details.

Value

Returns a stack of box-and-whisker plots of |CWRES| vs covariates.

Author(s)

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

See Also

xpose.plot.bw, xpose.panel.bw, compute.cwres, bwplot, xpose.prefs-class, xpose.data-class

Other specific functions: 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(), wres.vs.pred(), xpose.VPC.both(), xpose.VPC.categorical(), xpose.VPC(), xpose4-package

Examples

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

absval.cwres.vs.cov.bw(xpdb)

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