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Last data update: 2014.03.03

R: Prunning the Maximal tree

best.tree.CV

R Documentation

Prunning the Maximal tree

Description

this function is set to prune back the maximal tree by using a K-fold cross-validation procedure.

Usage

best.tree.CV(xtree, xdata, Y.name, X.names, G.names, family = "binomial", 
args.rpart = list(cp = 0, minbucket = 20, maxdepth = 10), epsi = 0.001, 
iterMax = 5, iterMin = 3, ncv = 10, 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 variables to consider in the linear part of the `glm`
`G.names`	the names of independent variables to consider in the tree part of the hybrid `glm`.
`family`	the `glm` family considered depending on the type of the dependent variable.
`args.rpart`	a list of options that control details of the rpart algorithm. `minbucket`: the minimum number of observations in any terminal <leaf> node; `cp`: complexity parameter (Any split that does not decrease the overall lack of fit by a factor of cp is not attempted); `maxdepth`: the maximum depth of any node of the final tree, with the root node counted as depth 0. ... See `rpart.control` for further details
`epsi`	a treshold value to check the convergence of the algorithm
`iterMax`	the maximal number of iteration to consider
`iterMin`	the minimum number of iteration to consider
`ncv`	The number of folds to consider for the `cross-validation`
`verbose`	Logical; TRUE for printing progress during the computation (helpful for debugging)

Value

a list of five elements:

`best_index`	The size of the selected tree by the cross-validation procedure
`tree`	The selected tree by `CV`
`fit_glm`	The fitted gpltr models selected with `CV`
`CV_ERRORS`	A list of two elements containing the cross-validation error of the selected tree by the `CV` procedure and a vector of cross-validation errors of all the competing models
`Timediff`	The execution time of the `Cross-Validation` procedure

Author(s)

Cyprien Mbogning

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)

Examples

## 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 a cross-validation procedure

tree_selected <- best.tree.CV(fit_pltr$tree, data_pltr, Y.name, X.names, G.names, 
     family = family, args.rpart = args.rpart, epsi = 0.001, iterMax = 5, 
     iterMin = 3, ncv = 10)
     
plot(tree_selected$tree, main = 'CV TREE')
text(tree_selected$tree, minlength = 0L, xpd = TRUE, cex = .6)

## End(Not run)