Several variable importance scores are computed: the deviance importance score (DIS), the permutation importance score (PIS), the depth deviance importance score (DDIS), the minimal depth importance score (MinDepth) and the occurence score (OCCUR).
Usage
VIMPBAG(BAGGRES, data, Y.name)
Arguments
BAGGRES
The output of the bagging procedure (bagging.pltr)
data
The learning dataframe used within the bagging procedure
Y.name
The name of the binary dependant variable used in the bagging procedure
Details
several choices for variable selection using the bagging procedure are proposed. A discussion about the scores of importance PIS, DIS, and DDIS is available in Mbogning et al. 2015
Value
A list with 9 elements
PIS
A list of length the length of the thresshold value used in the bagging procedure, containing the permutation importance score displayed in decreasing order for each thresshold value
StdPIS
The standard error of the PIS
OCCUR
The occurence number for each variable in the bagging sequence displayed in decreasing order
DIS
The deviance importance score displayed in decreasing order
DDIS
The depth deviance importance score displayed in decreasing order
MinDepth
The minimal depth score for each variable, displayed in increasing order
dimtrees
A vector containing the dimensions of trees within the baging sequence
EOOB
A vector containing the OOB error of the bagging procedure for each thresshold value
Bagfinal
The number of Bagging iterations used
Author(s)
Cyprien Mbogning
References
Mbogning, C., Perdry, H., Broet, P.: A Bagged partially linear tree-based regression procedure for prediction and variable selection. Human Heredity (To appear), (2015)
See Also
bagging.pltr
Examples
## Not run:
## load the data set
data(burn)
## set the parameters
args.rpart <- list(minbucket = 10, maxdepth = 4, cp = 0, maxsurrogate = 0)
family <- "binomial"
Y.name <- "D2"
X.names <- "Z2"
G.names <- c('Z1','Z3','Z4','Z5','Z6','Z7','Z8','Z9','Z10','Z11')
args.parallel = list(numWorkers = 1)
## Bagging a set of basic unprunned pltr predictors
Bag.burn <- bagging.pltr(burn, Y.name, X.names, G.names, family,
args.rpart,epsi = 0.01, iterMax = 4, iterMin = 3,
Bag = 20, verbose = FALSE, doprune = FALSE)
## Several importance scores for variables, using the bagging procedure
Var_Imp_BAG.burn <- VIMPBAG(Bag.burn, burn, Y.name)
## Importance score using the permutaion method for each thresshold value
Var_Imp_BAG.burn$PIS
## Importance score using the deviance criterion
Var_Imp_BAG.burn$DIS
## End(Not run)