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Dependent variable vs population predictions, conditioned on covariates, for Xpose 4

Source: R/dv.vs.pred.by.cov.R
dv.vs.pred.by.cov.Rd

This is a plot of the dependent variable (DV) 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

dv.vs.pred.by.cov(
  object,
  covs = "Default",
  abline = c(0, 1),
  smooth = TRUE,
  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.

abline

Vector of arguments to the panel.abline function. No abline is drawn if NULL.

smooth

Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.

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}.

Value

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

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.

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

See also

dv.vs.pred, xpose.plot.default, xpose.panel.default, xyplot, xpose.prefs-class, xpose.data-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.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, 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(), wres.vs.idv.bw(), wres.vs.pred(), wres.vs.pred.bw(), xpose.VPC(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Author

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

Examples

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