This creates a stack of plots of conditional weighted residuals (CWRES)
plotted against covariates, and is a specific function in Xpose 4. It is a
wrapper encapsulating arguments to the xpose.plot.default
and
xpose.plot.histogram
functions. Most of the options take their
default values from xpose.data object but may be overridden by supplying
them as arguments.
cwres.vs.cov(
object,
ylb = "CWRES",
smooth = TRUE,
type = "p",
main = "Default",
...
)
An xpose.data object.
A string giving the label for the y-axis. NULL
if none.
A NULL
value indicates that no superposed line should
be added to the graph. If TRUE
then a smooth of the data will be
superimposed.
1-character string giving the type of plot desired. The following values are possible, for details, see 'plot': '"p"' for points, '"l"' for lines, '"o"' for over-plotted points and lines, '"b"', '"c"') for (empty if '"c"') points joined by lines, '"s"' and '"S"' for stair steps and '"h"' for histogram-like vertical lines. Finally, '"n"' does not produce any points or lines.
The title of the plot. If "Default"
then a default title
is plotted. Otherwise the value should be a string like "my title"
or
NULL
for no plot title.
Other arguments passed to link{xpose.plot.default}
or
link{xpose.plot.histogram}
.
Returns a stack of xyplots and histograms of CWRES versus covariates.
Each of the covariates in the Xpose data object, as specified in
object@Prefs@Xvardef$Covariates
, is evaluated in turn, creating a
stack of plots.
Conditional weighted residuals (CWRES) require some extra steps to
calculate. See compute.cwres
for details.
A wide array of extra options controlling xyplots and histograms are
available. See xpose.plot.default
and
xpose.plot.histogram
for details.
xpose.plot.default
,
xpose.plot.histogram
, xyplot
,
histogram
, xpose.prefs-class
,
compute.cwres
, 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.iwres.vs.pred()
,
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.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
# NOT RUN {
## Here we load the example xpose database
xpdb <- simpraz.xpdb
cwres.vs.cov(xpdb)
# }
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