Absolute population weighted residuals vs population predictions, conditioned on covariates, for Xpose 4

This is a plot of absolute population weighted residuals (|WRES|) 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.wres.vs.pred.by.cov(
  object,
  ylb = "|WRES|",
  type = "p",
  smooth = TRUE,
  ids = FALSE,
  idsdir = "up",
  main = "Default",
  ...
)

Arguments

object: An xpose.data object.
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.
ids: Logical. Should id labels on points be shown?
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}.

Value

Returns a stack of xyplots of |WRES| 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 for details.

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_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.cov(), 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


if (FALSE) {
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

## A vanilla plot
absval.wres.vs.pred.by.cov(xpdb)

## Custom axis labels
absval.wres.vs.pred.by.cov(xpdb, ylb="|CWRES|", xlb="PRED")

## Custom colours and symbols, IDs
absval.wres.vs.pred.by.cov(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)
}