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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.

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.

Value

Delivers a stack of histograms.

Functions

  • cov.hist: Covariate distributions

  • parm.hist: parameter distributions

  • ranpar.hist: random parameter distributions

Author(s)

Andrew Hooker & Justin Wilkins

See Also

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

## 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)
xpose4

Diagnostics for Nonlinear Mixed-Effect Models

v4.7.1
LGPL (>= 3)
Authors
Andrew C. Hooker [aut, cre, cph], Mats O. Karlsson [aut, cph], Justin J. Wilkins [aut], E. Niclas Jonsson [aut, trl, cph], Ron Keizer [ctb] (functionality for bootstrap of GAM and SCM)
Initial release

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