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Last data update: 2014.03.03

R: parametric bootstrap on a pltr model

best.tree.bootstrap

R Documentation

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)

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)

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

Author(s)

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)

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="")
## Not run: 
## 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)
  
## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GPLTR)
Loading required package: rpart
Loading required package: parallel
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GPLTR/best.tree.bootstrap.Rd_%03d_medium.png", width=480, height=480)
> ### Name: best.tree.bootstrap
> ### Title: parametric bootstrap on a pltr model
> ### Aliases: best.tree.bootstrap
> ### Keywords: documentation test
> 
> ### ** 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="")
> ## Not run: 
> ##D ## build a maximal tree
> ##D 
> ##D fit_pltr <- pltr.glm(data_pltr, Y.name, X.names, G.names, 
> ##D    args.rpart = args.rpart, family = family, iterMax = 5, iterMin = 3)
> ##D                      
> ##D ## select an test the selected tree by a parametric bootstrap procedure
> ##D args.parallel = list(numWorkers = 1, type = "PSOCK")
> ##D 
> ##D best_bootstrap <- best.tree.bootstrap(fit_pltr$tree, data_pltr, Y.name, X.names, 
> ##D   G.names, B = 10, BB = 10, args.rpart = args.rpart, epsi = 0.001, 
> ##D   iterMax = 5, iterMin = 3, family = family, LEVEL = 0.05,LB = FALSE, 
> ##D   args.parallel = args.parallel)
> ##D   
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>