xpose4 (version 4.7.1)

par_cov_hist: Plot the parameter or covariate distributions using a histogram

Description

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

Usage

cov.hist(object, onlyfirst = TRUE, main = "Default", ...)
parm.hist(object, onlyfirst = TRUE, main = "Default", ...)
ranpar.hist(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.histogram.

Value

Delivers a stack of histograms.

Functions

• cov.hist: Covariate distributions

• parm.hist: parameter distributions

• ranpar.hist: random parameter distributions

Details

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

A wide array of extra options controlling histograms are available. See xpose.plot.histogram for details.

See Also

xpose.plot.histogram, xpose.panel.histogram, histogram, xpose.data-class, xpose.prefs-class

Other specific functions: absval.cwres.vs.cov.bw(), 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_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

# NOT RUN {
## Here we load the example xpose database 
xpdb <- simpraz.xpdb

## Parameter histograms
parm.hist(xpdb)

## Covariate distribution, in green
cov.hist(xpdb, hicol=11, hidcol="DarkGreen", hiborder="White")

## Random parameter histograms
ranpar.hist(xpdb)

# }