predict_bagg.pltr {GPLTR}

prediction on new features

Description

Prediction on new features using a set of bagging pltr models

Usage

predict_bagg.pltr(bag_pltr, Y.name, newdata, type = "response",
                  thresshold = seq(0, 1, by = 0.1))

Arguments

bag_pltr

the bagging result obtained with the function bagging.pltr

Y.name

the name of the binary dependent variable

newdata

a data frame in which to look for predictors and the dependant variable.

type

the type of prediction required. type = "response" is the default; It gives the predicted probabilities. At this stage of the package, only this type is take into account. Other types such as "link" and "terms" are useless.

thresshold

a vector of cutoff values for binary prediction. Could be helpfull for computing the AUC on the test sample.

Value

A list with 8 elements

FINAL_PRED_IND1

A list of size the length of the thresshold vector, containing the final prediction of each individual of the testing data by the bagging procedure using the majority rule (the modal prediction).

FINAL_PRED_IND2

A list of size the length of the thresshold vector, containing the final prediction of each individual of the testing data by the bagging procedure using the mean estimated probability.

PRED_ERROR1

A vector of estimated errors of the Bagging procedure on the test sample for each thresshold value using FINAL_PRED_IND1.

PRED_ERROR2

A vector of estimated errors of the Bagging procedure on the test sample for each thresshold value using FINAL_PRED_IND2.

CONF1

A list of confusion matrix using FINAL_PRED_IND1

CONF2

A list of confusion matrix using FINAL_PRED_IND2

PRED_ERRORS_PBP

A list of size the length of the thresshold vector. Each element representing the prediction error obtained via each predictor in the bagging sequence for each thresshold value

PRED_ERROR_PBP

A vector containing the mean of PRED_ERRORS_PBP for each thresshold value

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, predict.glm

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)

## Use the bagging procedure to predict new features

# ?predict_bagg.pltr

 Pred_Bag.burn <- predict_bagg.pltr(Bag.burn, Y.name, newdata = burn, 
                type = "response", thresshold = seq(0, 1, by = 0.1))

## The confusion matrix for each thresshold value using the majority vote

Pred_Bag.burn$CONF1

## End(Not run)