nested.trees: compute the nested trees

In GPLTR: Generalized Partially Linear Tree-Based Regression Model

compute the nested trees

Description

Compute a sequence of nested competing trees for the prunning step

Usage

nested.trees(xtree, xdata, Y.name, X.names, MaxTreeSize = NULL,
family = "binomial", verbose = TRUE)

Arguments

xtree	a tree inheriting to the rpart method
xdata	the dataset used to build the tree
Y.name	the name of the dependent variable in the tree model
X.names	the names of independent variables considered as offset in the tree model
MaxTreeSize	The maximal size of the competing trees
family	the glm family considered depending on the type of the dependent variable.
verbose	Logical; TRUE for printing progress during the computation (helpful for debugging)

Value

a list with 4 elements:

leaves	a list of leaves of the competing trees to consider for the optimal tree
null_deviance	the deviance of the null model (linear part of the glm)
deviances	a vector of deviances of the competing PLTR models
diff_deviances	a vector of the deviance differencies between the competing PLTR models and the null model

Author(s)

Cyprien Mbogning and Wilson Toussile

Examples

## Not run: 
## load the data set

data(data_pltr)
args.rpart <- list(minbucket = 40, maxdepth = 10, cp = 0)
family <- "binomial"
Y.name <- "Y"
X.names <- "G1"
G.names <- paste("G", 2:15, sep="")

## build a maximal tree

fit_pltr <- pltr.glm(data_pltr, Y.name, X.names, G.names, args.rpart = args.rpart, 
                     family = family,iterMax = 5, iterMin = 3)
                     
## compute the competing trees

nested_trees <- nested.trees(fit_pltr$tree, data_pltr, Y.name, X.names, 
                            MaxTreeSize = 10, family = family)

## End(Not run)

GPLTR documentation built on Aug. 27, 2023, 1:06 a.m.