This creates an eta correlation which defines the relationship between parameters
eta_pairs(
is.title,
title,
dname = NULL,
type.eta = c("mode", "mean"),
text_color = "black",
is.shrink = TRUE,
is.smooth = TRUE,
smooth = NULL,
point = NULL,
shrink = NULL,
is.hline = FALSE,
hline = NULL,
is.vreference_line = FALSE,
vreference_line = list(colour = "orange", linetype = "longdash"),
...
)
ecorrel object
logical
if TRUE then a title is used for the plot
character the plot title
name of dataset to be used
character
type of eat can be 'mode' or 'mean'.'mode' by default
color of the correlation text in the upper matrix
logical
if TRUE add shrinkage to the plot
logical
if TRUE add smoothing to lower matrix plots
list
geom_smooth graphical parameters
list
geom_point graphical parameter
pmxShrinkClass
shrinkage graphical parameter or
list
coercible into one
logical
if TRUE add horizontal line to lower matrix plots
list
geom_hline graphical parameters
logical
if TRUE add the +- 1.96 lines
list
geom_hline graphical parameters for the reference lines
others graphics arguments passed to pmx_gpar
internal object.
Other plot_pmx:
distrib()
,
eta_cov()
,
individual()
,
plot_pmx.distrib()
,
plot_pmx.eta_cov()
,
plot_pmx.eta_pairs()
,
plot_pmx.individual()
,
plot_pmx.pmx_dens()
,
plot_pmx.pmx_qq()
,
plot_pmx.residual()
,
plot_pmx_gpar_real()
,
plot_pmx()