Plot scatterplot matrices of parameters, random parameters or covariates
These functions plot scatterplot matrices of parameters, random parameters and covariates.
cov.splom(
object,
main = xpose.multiple.plot.title(object = object, plot.text =
"Scatterplot matrix of covariates", ...),
varnames = NULL,
onlyfirst = TRUE,
smooth = TRUE,
lmline = NULL,
...
)
parm.splom(
object,
main = xpose.multiple.plot.title(object = object, plot.text =
"Scatterplot matrix of parameters", ...),
varnames = NULL,
onlyfirst = TRUE,
smooth = TRUE,
lmline = NULL,
...
)
ranpar.splom(
object,
main = xpose.multiple.plot.title(object = object, plot.text =
"Scatterplot matrix of random parameters", ...),
varnames = NULL,
onlyfirst = TRUE,
smooth = TRUE,
lmline = NULL,
...
)object |
An xpose.data object. |
main |
A string giving the plot title or |
varnames |
A vector of strings containing labels for the variables in the scatterplot matrix. |
onlyfirst |
Logical value indicating if only the first row per individual is included in the plot. |
smooth |
A |
lmline |
logical variable specifying whether a linear regression line
should be superimposed over an |
... |
Other arguments passed to |
The parameters or covariates in the Xpose data object, as specified in
object@Prefs@Xvardef$parms, object@Prefs@Xvardef$ranpar or
object@Prefs@Xvardef$covariates, are plotted together as scatterplot
matrices.
A wide array of extra options controlling scatterplot matrices are
available. See xpose.plot.splom for details.
To control the appearance of the labels and names in the scatterplot matrix
plots you can try varname.cex=0.5 and axis.text.cex=0.5 (this
changes the tick labels and the variable names to be half as large as
normal).
Delivers a scatterplot matrix.
cov.splom: A scatterplot matrix of covariates
parm.splom: A scatterplot matrix of parameters
ranpar.splom: A scatterplot matrix of random parameters
Andrew Hooker & Justin Wilkins
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(),
cwres.dist.hist(),
cwres.dist.qq(),
cwres.vs.cov(),
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
## Here we load the example xpose database xpdb <- simpraz.xpdb ## A scatterplot matrix of parameters, grouped by sex parm.splom(xpdb, groups="SEX") ## A scatterplot matrix of ETAs, grouped by sex ranpar.splom(xpdb, groups="SEX") ## Covariate scatterplots, with text customization cov.splom(xpdb, varname.cex=0.4, axis.text.cex=0.4, smooth=NULL, cex=0.4)
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