Absolute population weighted residuals vs population predictions for Xpose 4

This is a plot of absolute population weighted residuals (|WRES|) vs population predictions (PRED), 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.

absval.wres.vs.pred(
  object,
  ylb = "|WRES|",
  idsdir = "up",
  type = "p",
  smooth = TRUE,
  ...
)

Arguments

object

An xpose.data object.

ylb

A string giving the label for the y-axis. NULL if none.

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.

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 |WRES| vs PRED.

Details

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

See also

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.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) { # \dontrun{
## 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(xpdb)


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


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


## Custom colours and symbols
absval.wres.vs.pred(xpdb, cex=0.6, pch=19, col=1, 
  smcol="blue", smlty=2)