This creates a stack of four plots, comparing PRED, IPRED, WRES (or CWRES), and IWRES estimates for the two specified model fits.
Usage
basic.model.comp(
object,
object.ref = NULL,
onlyfirst = FALSE,
inclZeroWRES = FALSE,
subset = xsubset(object),
main = "Default",
force.wres = FALSE,
...
)
Arguments
- object
An xpose.data object.
- object.ref
An xpose.data object. If not supplied, the user will be prompted.
- onlyfirst
Logical value indicating whether only the first row per individual is included in the plot.
- inclZeroWRES
Logical value indicating whether rows with WRES=0 is included in the plot. The default is TRUE.
- subset
A string giving the subset expression to be applied to the data before plotting. See
xsubset
.- main
The title of the plot. If
"Default"
then a default title is plotted. Otherwise the value should be a string like"my title"
orNULL
for no plot title.- force.wres
Force function to use WRES?
- ...
Other arguments passed to
link{xpose.plot.default}
.
Value
Returns a stack of plots comprising comparisons of PRED, IPRED, WRES (or CWRES) and IWRES for the two specified runs.
Details
Four basic model comparison plots are displayed in sequence.
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
for details.
See also
xpose.plot.default
,
xpose.panel.default
, xyplot
,
compute.cwres
, 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()
,
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()
,
wres.vs.pred.bw()
,
xpose.VPC()
,
xpose.VPC.both()
,
xpose.VPC.categorical()
,
xpose4-package
Examples
if (FALSE) {
## We expect to find the required NONMEM run and table files for runs
## 5 and 6 in the current working directory
xpdb5 <- xpose.data(5)
xpdb6 <- xpose.data(6)
## A vanilla plot, without prompts
basic.model.comp(xpdb5, xpdb6, prompt = FALSE)
## Custom colours and symbols, no user IDs
basic.model.comp.cwres(xpdb5, xpdb6, cex=0.6, pch=8, col=1, ids=NULL)
}