This creates a stack of four plots, comparing PRED, IPRED, WRES (or CWRES), and IWRES estimates for the two specified model fits.
basic.model.comp(
object,
object.ref = NULL,
onlyfirst = FALSE,
inclZeroWRES = FALSE,
subset = xsubset(object),
main = "Default",
force.wres = FALSE,
...
)An xpose.data object.
An xpose.data object. If not supplied, the user will be prompted.
Logical value indicating whether only the first row per individual is included in the plot.
Logical value indicating whether rows with WRES=0 is included in the plot. The default is TRUE.
A string giving the subset expression to be applied to the
data before plotting. See xsubset.
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.
Force function to use WRES?
Other arguments passed to link{xpose.plot.default}.
Returns a stack of plots comprising comparisons of PRED, IPRED, WRES (or CWRES) and IWRES for the two specified runs.
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.
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
if (FALSE) { # \dontrun{
## 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)
} # }