Returns the covariates used to split a recursive partitioning tree and the rules that were applied to build the tree
node_labels(x)
node_rules(x)
top_items(x, top = 5)
# S3 method for pltree
plot(x, log = TRUE, ref = NULL, ci.level = 0.95, ...)
an object of class party
an integer for the number of items to return
logical, if TRUE
log-worth coefficients are
displayed instead of worth
optional, character for the reference item when
log = TRUE
an integer for the confidence interval levels
additional arguments passed to methods. See details
a vector with the node labels, a data.frame with node rules, a ggplot
Argument multcomp = TRUE adds multi-comparison letters from multcompView
# \donttest{
library("PlackettLuce")
data("beans", package = "PlackettLuce")
G = rank_tricot(data = beans,
items = c(1:3),
input = c(4:5),
group = TRUE,
additional.rank = beans[c(6:8)])
pld = cbind(G, beans[,c("maxTN", "season", "lon")])
tree = pltree(G ~ maxTN + season + lon, data = pld)
node_labels(tree)
node_rules(tree)
top_items(tree)
plot(tree)
plot(tree, log = TRUE)
# }