cwres.vs.idv {xpose4}

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

Description

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}.

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.

Value

Returns an xyplot of CWRES vs IDV.

Author(s)

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

See Also

xpose.plot.default, xpose.panel.default, 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.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

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")