rf.fs: Random Forest Cross-Valdidation for feature selection

In BioMedR: Generating Various Molecular Representations for Chemicals, Proteins, DNAs/RNAs and Their Interactions

Description

Random Forest Cross-Valdidation for feature selection

Usage

rf.fs(trainx, trainy, cv.fold = 5, scale = "log", step = 0.5,
  mtry = function(p) max(1, floor(sqrt(p))), recursive = FALSE)

Arguments

trainx	matrix or data frame containing columns of predictor variables
trainy	vector of response, must have length equal to the number of rows in trainx
cv.fold	The fold, the defalut is 5.
scale	If "log", reduce a fixed proportion (step) of variables at each step, otherwise reduce step variables at a time
step	If log=TRUE, the fraction of variables to remove at each step, else remove this many variables at a time
mtry	A function of number of remaining predictor variables to use as the mtry parameter in the randomForest call
recursive	Whether variable importance is (re-)assessed at each step of variable reduction

Details

This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.

Value

A list with the following three components::

• n.var - vector of number of variables used at each step

• error.cv - corresponding vector of error rates or MSEs at each step

• res - list of n.var components, each containing the feature importance values from the cross-validation

Author(s)

Min-feng Zhu <[e-mail]>

References

Svetnik, V., Liaw, A., Tong, C. and Wang, T., Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules, MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334-343.

See Also

See rf.cv for the Cross-Validation of Classification and Regression models using Random Forest

Examples

training = read.csv(system.file('sysdata/training1.csv', package = 'BioMedR'), header = TRUE)
y = training[, 1]
x = training[, -1]
result = rf.fs(x, y)

BioMedR documentation built on Nov. 17, 2017, 10:08 a.m.