Population conditional weighted residuals (CWRES) plotted against the independent variable (IDV) for Xpose 4

This is a plot of population conditional weighted residuals (CWRES) vs the independent variable (IDV), 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

cwres.vs.idv(object, abline = c(0, 0), smooth = TRUE, ...)

Arguments

object: An xpose.data object.
abline: Vector of arguments to the panel.abline function. No abline is drawn if NULL.
smooth: A NULL value indicates that no superposed line should be added to the graph. If TRUE then a smooth of the data will be superimposed.
...: Other arguments passed to link{xpose.plot.default}.

Value

Returns an xyplot of CWRES vs IDV.

Details

Conditional weighted residuals (CWRES) are plotted against the independent variable, as specified in object@Prefs@Xvardef$idv.

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

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

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

## A vanilla plot
cwres.vs.idv(xpdb)


## A conditioning plot
cwres.vs.idv(xpdb, by="HCTZ")