| cwres.vs.pred {xpose4} | R Documentation |
Population conditional weighted residuals (CWRES) plotted against population predictions (PRED) for Xpose 4
Description
This is a plot of population conditional weighted residuals (cwres) 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.
Usage
cwres.vs.pred(object, abline = c(0, 0), smooth = TRUE, ...)
Arguments
object |
An xpose.data object. |
abline |
Vector of arguments to the |
smooth |
Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE. |
... |
Other arguments passed to |
Details
Conditional weighted residuals (CWRES) require some extra steps to
calculate. See compute.cwres for details.
A wide array of extra options controlling xyplots are available. See
xpose.plot.default and xpose.panel.default for
details.
Value
Returns an xyplot of CWRES vs PRED.
Author(s)
E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins
See Also
xpose.plot.default, xyplot,
xpose.prefs-class, compute.cwres,
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(),
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.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
Examples
## Here we load the example xpose database
xpdb <- simpraz.xpdb
cwres.vs.pred(xpdb)
## A conditioning plot
cwres.vs.pred(xpdb, by="HCTZ")