VIMPBAG {GPLTR}

score of importance for variables

Description

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)