best.tree.permute: permutation test on a pltr model

In GPLTR: Generalized Partially Linear Tree-Based Regression Model

permutation test on a pltr model

Description

a unified permutation test procedure to select and test at the same time the selected tree

Usage

best.tree.permute(xtree, xdata, Y.name, X.names, G.names, B = 10,
 args.rpart = list(cp = 0, minbucket = 20, maxdepth = 10), epsi = 0.001,
 iterMax = 5, iterMin = 3, family = "binomial", LEVEL = 0.05, 
 LB = FALSE, args.parallel = list(numWorkers = 1, type = "PSOCK"), verbose = TRUE)

Arguments

xtree	the maximal tree obtained by the function pltr.glm
xdata	the data frame used to build xtree
Y.name	the name of the dependent variable
X.names	the names of independent variables to consider in the linear part of the glm. For this function, only a binary variable is supported.
G.names	the names of independent variables to consider in the tree part of the hybrid glm.
B	the size of the bootstrap sample
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
family	the binomial family.
LEVEL	the level of the test
LB	a binary indicator with values TRUE or FALSE indicating weither the loading is balanced or not in the parallel computing. It is useless on a windows platform.
args.parallel	parameters of the parallelization. See mclapply for more details.
verbose	Logical; TRUE for printing progress during the computation (helpful for debugging)

Value

a list with six elements:

p.val_selected	the adjusted p-value of the selected tree
selected_model	a list with the fit of the selected pltr model fit_glm, the selected tree tree and the p-value of the selected tree without adjusting for multiple comparisons p.value
fit_glm	the fitted pltr model under the null hypothesis if the test is not significant
Timediff	The execution time of the permutation test procedure
comp_p_values	The P-values of the competing trees
Badj	The number of samples used inside the procedure

Author(s)

Cyprien Mbogning

See Also

p.val.tree, best.tree.bootstrap

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)
                     
## select an test the selected tree by a permutation test procedure

args.parallel = list(numWorkers = 1, type = "PSOCK")

best_permute <- best.tree.permute(fit_pltr$tree, data_pltr, Y.name, X.names,
  G.names, B = 10, args.rpart = args.rpart, epsi = 0.001, iterMax = 5,              
             iterMin = 3, family = family, LEVEL = 0.05,LB = FALSE, 

## End(Not run)                                   args.parallel = args.parallel)

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