View source: R/absval.cwres.vs.pred.by.cov.R
absval.cwres.vs.pred.by.cov | R Documentation |
This is a plot of absolute population conditional weighted residuals
(|CWRES|) vs population predictions (PRED) conditioned by covariates, 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.
absval.cwres.vs.pred.by.cov(
object,
covs = "Default",
ylb = "|CWRES|",
type = "p",
smooth = TRUE,
idsdir = "up",
main = "Default",
...
)
object |
An xpose.data object. |
covs |
A vector of covariates to use in the plot. If "Default" the
the covariates defined in |
ylb |
A string giving the label for the y-axis. |
type |
Type of plot. The default is points only ("p"), but lines ("l") and both ("b") are also available. |
smooth |
Logical value indicating whether an x-y smooth should be superimposed. The default is TRUE. |
idsdir |
Direction for displaying point labels. The default is "up", since we are displaying absolute values. |
main |
The title of the plot. If |
... |
Other arguments passed to |
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.
The main
argument is not supported owing to the multiple plots
generated by the function.
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.
Returns a stack of xyplots of |CWRES| vs PRED, conditioned on covariates.
E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins
absval.cwres.vs.pred
,
xpose.plot.default
, xpose.panel.default
,
xyplot
, xpose.prefs-class
,
compute.cwres
, xpose.data-class
Other specific functions:
absval.cwres.vs.cov.bw()
,
absval.cwres.vs.pred()
,
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()
,
wres.vs.pred.bw()
,
xpose.VPC()
,
xpose.VPC.both()
,
xpose.VPC.categorical()
,
xpose4-package
absval.cwres.vs.pred.by.cov(simpraz.xpdb, covs=c("HCTZ","WT"), max.plots.per.page=2)
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