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)