Absolute individual weighted residuals vs individual predictions for Xpose 4

This is a plot of absolute individual weighted residuals (|IWRES|) vs individual predictions (IPRED), 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.iwres.vs.ipred(
  object,
  ylb = "|iWRES|",
  type = "p",
  ids = FALSE,
  idsdir = "up",
  smooth = TRUE,
  ...
)

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.
ids: Should id values be displayed?
idsdir: Direction for displaying point labels. The default is "up", since we are displaying absolute values.
smooth: Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE.
...: Other arguments passed to link{xpose.plot.default}.

Value

Returns an xyplot of |IWRES| vs IPRED.

Details

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.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.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


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

## A vanilla plot
absval.iwres.vs.ipred(xpdb)


## A conditioning plot
absval.iwres.vs.ipred(xpdb, by="HCTZ")


## Custom heading and axis labels
absval.iwres.vs.ipred(xpdb, main="My conditioning plot", ylb="|IWRES|", xlb="IPRED")


## Custom colours and symbols, no IDs
absval.iwres.vs.ipred(xpdb, cex=0.6, pch=3, col=1, ids=FALSE)