Absolute individual weighted residuals vs population predictions or independent variable for Xpose 4

This is a plot of absolute individual weighted residuals (|IWRES|) vs individual predictions (PRED) or independent variable (IDV), specific functions in Xpose 4. These functions are wrappers 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.pred(
  object,
  ylb = "|IWRES|",
  smooth = TRUE,
  idsdir = "up",
  type = "p",
  ...
)

Arguments

object: An xpose.data object.
ylb: A string giving the label for the y-axis. NULL if none.
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.
type: Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available.
...: Other arguments passed to link{xpose.plot.default}.

Value

Returns an xyplot of |IWRES| vs PRED or |IWRES| vs IDV.

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(), absval.iwres.vs.ipred.by.cov(), 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


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.iwres.vs.pred(xpdb)


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


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


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