par_cov_qq {xpose4}

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

Description

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.

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.

Value

Delivers a stack of Q-Q plots.

Functions

• cov.qq(): Covariate distributions

• parm.qq(): parameter distributions

• ranpar.qq(): random parameter distributions

Author(s)

Andrew Hooker & Justin Wilkins

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

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)