xpose4: absval.cwres.vs.pred.by.cov – R documentation

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absval.cwres.vs.pred.by.cov

Absolute value of the conditional weighted residuals vs. population predictions, conditioned on covariates, for Xpose 4

Description

This is a plot of absolute population conditional weighted residuals (|CWRES|) vs population predictions (PRED) conditioned by covariates, a specific function in Xpose 4. It is a wrapper encapsulating arguments to the xpose.plot.default function. Most of the options take their default values from xpose.data object but may be overridden by supplying them as arguments.

Usage

absval.cwres.vs.pred.by.cov(
  object,
  covs = "Default",
  ylb = "|CWRES|",
  type = "p",
  smooth = TRUE,
  idsdir = "up",
  main = "Default",
  ...
)

Arguments

`object`	An xpose.data object.
`covs`	A vector of covariates to use in the plot. If "Default" the the covariates defined in `object@Prefs@Xvardef$Covariates` are used.
`ylb`	A string giving the label for the y-axis. `NULL` if none.
`type`	Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
`smooth`	Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
`idsdir`	Direction for displaying point labels. The default is "up", since we are displaying absolute values.
`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 `link{xpose.plot.default}`.

Details

Each of the covariates in the Xpose data object, as specified in object@Prefs@Xvardef$Covariates, is evaluated in turn, creating a stack of plots.

The main argument is not supported owing to the multiple plots generated by the function.

Conditional weighted residuals (CWRES) require some extra steps to calculate. See compute.cwres for details.

A wide array of extra options controlling xyplots are available. See xpose.plot.default for details.

Value

Returns a stack of xyplots of |CWRES| vs PRED, conditioned on covariates.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

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

absval.cwres.vs.pred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)

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