Plot the parameter or covariate distributions using quantile-quantile (Q-Q) plots

These functions plot the parameter or covariate values stored in an Xpose data object using Q-Q plots.

Usage

cov.qq(object, onlyfirst = TRUE, main = "Default", ...)

parm.qq(object, onlyfirst = TRUE, main = "Default", ...)

ranpar.qq(object, onlyfirst = TRUE, main = "Default", ...)

Arguments

object

An xpose.data object.

onlyfirst

Logical value indicating if only the first row per individual is included in the plot.

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.qq.

Value

Delivers a stack of Q-Q plots.

Details

Each of the parameters or covariates in the Xpose data object, as specified in object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar or object@Prefs@Xvardef$covariates, is evaluated in turn, creating a stack of Q-Q plots.

A wide array of extra options controlling Q-Q plots are available. See xpose.plot.qq for details.

Functions

• cov.qq(): Covariate distributions

• parm.qq(): parameter distributions

• ranpar.qq(): random parameter distributions

See also

xpose.plot.qq, xpose.panel.qq, qqmath, xpose.data-class, xpose.prefs-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.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, 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

Andrew Hooker & Justin Wilkins

Examples

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

## parameter histograms
parm.qq(xpdb)



## A stack of random parameter histograms
ranpar.qq(xpdb)


## Covariate distribution, in green with red line of identity
cov.qq(xpdb, col=11, ablcol=2)