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()