multilevelPSA (version 1.2.5)

tree.plot: Heat map representing variables used in a conditional inference tree across level 2 variables.

Description

This figure provides a summary of the covariates used within each level two cluster along with their relative importance. Covariates are listed on the y-axis and level two clusters along the x-axis. Cells that are shaded indicate that that covariate was present in the conditional. The shade of the color represents the highest level within the tree that covariate appeared. That is, the darkest color, or depth 1, corresponds to the covariate used at the root of the tree, or the first split.

Usage

tree.plot(x, colNames, level2Col, colLabels = NULL, color.high = "azure",
  color.low = "steelblue", color.na = "white", ...)

Arguments

x

the results of mlpsa.ctree

colNames

the columns to include in the graphic

level2Col

the name of the level 2 column.

colLabels

column labels to use. This is a data frame with two columns, the first column should match the values in trees and the second column the description that will be used for labeling the variables.

color.high

color for variables with less relative importance as determined by occurring later in the tree (further from the root split).

color.low

color for variables with greater relative importance as determined by occurring sooner in the tree (closer to the root split).

color.na

color for variables that do not occur in the tree.

...

currently unused.

Value

a ggplot2 expression

See Also

plot.mlpsa

Examples

# NOT RUN {
require(party)
data(pisana)
data(pisa.colnames)
data(pisa.psa.cols)
mlctree = mlpsa.ctree(pisana[,c('CNT','PUBPRIV',pisa.psa.cols)], formula=PUBPRIV ~ ., level2='CNT')
student.party = getStrata(mlctree, pisana, level2='CNT')
tree.plot(mlctree, level2Col=pisana$CNT)
# }