GPLTR (version 1.5)

best.tree.bootstrap: parametric bootstrap on a pltr model

Description

a parametric bootstrap procedure to select and test at the same time the selected tree

Usage

best.tree.bootstrap(xtree, xdata, Y.name, X.names, G.names, B = 10, BB = 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), verbose = TRUE)

Value

a list with six elements

selected_model

a list with the fit of the selected pltr model fit_glm, the selected tree tree, the p-value of the selected tree p.value, the ajusted p-value of the selected tree adj_p.value and an indicator Tree_Selected to assess wether the test is significant or not.

fit_glm

the fitted pltr model under the null hypothesis if the test is not significant

Timediff

The execution time of the parametric bootstrap procedure

comp_p_values

The P-values of the competing trees

Badj

The number of samples used in the inner level of the procedure

BBadj

The number of samples used in the outer level of the procedure

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

G.names

the names of independent variables to consider in the tree part of the hybrid glm.

B

the size of the bootstrap sample

BB

the size of the bootstrap sample to compute the adjusted p-value

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 glm family considered depending on the type of the dependent variable.

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)

Author

Cyprien Mbogning and Wilson Toussile

References

Chen, J., Yu, K., Hsing, A., Therneau, T.M.: A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genetic Epidemiology 31, 238-251 (2007)

See Also

p.val.tree

Examples

#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="")
if (FALSE) {
## 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 parametric bootstrap procedure
args.parallel = list(numWorkers = 1, type = "PSOCK")

best_bootstrap <- best.tree.bootstrap(fit_pltr$tree, data_pltr, Y.name, X.names, 
  G.names, B = 10, BB = 10, args.rpart = args.rpart, epsi = 0.001, 
  iterMax = 5, iterMin = 3, family = family, LEVEL = 0.05,LB = FALSE, 
  args.parallel = args.parallel)
  }