best.tree.BIC.AIC: Prunning the Maximal tree

In GPLTR: Generalized Partially Linear Tree-Based Regression Model

Prunning the Maximal tree

Description

this function is set to prune back the maximal tree by using the BIC or the AIC criterion.

Usage

best.tree.BIC.AIC(xtree, xdata, Y.name, X.names,
                  family = "binomial", verbose = TRUE)

Arguments

xtree	a tree to prune
xdata	the dataset used to build the tree
Y.name	the name of the dependent variable
X.names	the names of independent confounding variables to consider in the linear part of the glm
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 of four elements:

best_index	The size of the selected trees by BIC and AIC
tree	The selected trees by BIC and AIC
fit_glm	The fitted pltr models selected with BIC, and AIC
Timediff	The execution time of the selection procedure

Author(s)

Cyprien Mbogning and Wilson Toussile

References

Mbogning, C., Perdry, H., Toussile, W., Broet, P.: A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities. Journal of Clinical Bioinformatics 4:6, (2014)

Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automat. Control AC-19, 716-723 (1974)

Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6, 461-464 (1978)

See Also

best.tree.CV, pltr.glm

Examples

data(burn)

args.rpart <- list(minbucket = 10, maxdepth = 4, cp = 0, maxcompete = 0, 
                    maxsurrogate = 0)
 family <- "binomial"
 X.names = "Z2"
 Y.name = "D2"
 G.names = c('Z1','Z3','Z4','Z5','Z6','Z7','Z8','Z9','Z10','Z11')
 
pltr.burn <- pltr.glm(burn, Y.name, X.names, G.names, args.rpart = args.rpart,
                   family = family, iterMax = 4, iterMin = 3, verbose = FALSE)

## Prunned back the maximal tree using either the BIC or the AIC criterion

pltr.burn_prun <- best.tree.BIC.AIC(xtree = pltr.burn$tree, burn, Y.name, 
                                    X.names, family = family)

## plot the BIC selected tree

plot(pltr.burn_prun$tree$BIC, main = 'BIC selected tree')
text(pltr.burn_prun$tree$BIC, xpd = TRUE, cex = .6, col = 'blue')

## Not run: 
##load the data set

data(data_pltr)

## Set the parameters

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)
                     
##prunned back the maximal tree by BIC or AIC criterion

tree_select <- best.tree.BIC.AIC(xtree = fit_pltr$tree,data_pltr,Y.name, 
                                 X.names, family = family)
                            
plot(tree_select$tree$BIC, main = 'BIC TREE')
text(tree_select$tree$BIC, minlength = 0L, xpd = TRUE, cex = .6)


## End(Not run)

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