| pred.vs.idv {xpose4} | R Documentation |
Population predictions (PRED) plotted against the independent variable (IDV) for Xpose 4
Description
This is a plot of population predictions (PRED) vs the independent variable
(IDV), 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
pred.vs.idv(object, smooth = TRUE, ...)
Arguments
object |
An xpose.data object. |
smooth |
Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE. |
... |
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 PRED 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(),
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(),
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
pred.vs.idv(xpdb)
## A conditioning plot
pred.vs.idv(xpdb, by="HCTZ")
## Logarithmic Y-axis
pred.vs.idv(xpdb, logy=TRUE)
## Custom colours and symbols, IDs
pred.vs.idv(xpdb, cex=0.6, pch=3, col=1, ids=TRUE)