absval.iwres.vs.pred: Absolute individual weighted residuals vs population...

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

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

Description

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}.

Details

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

Value

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

Author(s)

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

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.by.cov(), absval.cwres.vs.pred(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.ipred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred.by.cov(), 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.bw(), cwres.vs.idv(), cwres.vs.pred.bw(), cwres.vs.pred(), 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.by.cov(), dv.vs.ipred.by.idv(), dv.vs.ipred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), dv.vs.pred(), gof(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), ind.plots(), 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.bw(), wres.vs.idv(), wres.vs.pred.bw(), wres.vs.pred(), xpose.VPC.both(), xpose.VPC.categorical(), xpose.VPC(), xpose4-package

Examples

## Not run: 
## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb5 <- xpose.data(5)

## End(Not run)

## 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)

xpose4 documentation built on May 31, 2022, 5:07 p.m.