| wres.vs.pred {xpose4} | R Documentation |
Population weighted residuals (WRES) plotted against population predictions (PRED) for Xpose 4
Description
This is a plot of 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.
Usage
wres.vs.pred(object, smooth = TRUE, abline = c(0, 0), ...)
Arguments
object |
An xpose.data object. |
smooth |
Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE. |
abline |
Vector of arguments to the |
... |
Other arguments passed to |
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 WRES 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(),
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.bw(),
xpose.VPC(),
xpose.VPC.both(),
xpose.VPC.categorical(),
xpose4-package
Examples
## Here we load the example xpose database
xpdb <- simpraz.xpdb
wres.vs.pred(xpdb)
## A conditioning plot
wres.vs.pred(xpdb, by="HCTZ")