xpose4: cwres.wres.vs.pred – R documentation

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cwres.wres.vs.pred

Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the population predictions (PRED)

Description

Graphically compares WRES and CWRES as plotted against the population predictions.Conditional weighted residuals (CWRES) require some extra steps to calculate. Either add CWRES to your NONMEM table files or compute them using the information proveded in compute.cwres. A wide array of extra options controlling xyplots are available. See xpose.plot.default and xpose.panel.default for details.

Usage

cwres.wres.vs.pred(
  object,
  ylb = "Residuals",
  abline = c(0, 0),
  smooth = TRUE,
  scales = list(),
  ...
)

Arguments

`object`	An xpose.data object.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`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.
`scales`	scales is passed to `xpose.plot.default`
`...`	Other arguments passed to `xpose.plot.default`.

Value

A compound xyplot.

Author(s)

Niclas Jonsson & Andrew Hooker

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(), 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_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.bw(), wres.vs.idv(), wres.vs.pred.bw(), wres.vs.pred(), xpose.VPC.both(), xpose.VPC.categorical(), xpose.VPC(), xpose4-package

Examples

cwres.wres.vs.pred(simpraz.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