rrf.once: Feature selection by regularized random forest and compare...

In KnowGRRF: Knowledge-Based Guided Regularized Random Forest

Description

Select features using regularized random forest model. Build random forest model either using or not using feature selection. Compare model performance on an independent test set.

Usage

rrf.once(X.train, Y.train, X.test, Y.test, coefReg)

Arguments

X.train	a data frame or matrix (like x) containing predictors for the training set.
Y.train	response for the training set. If a factor, classification is assumed, otherwise regression is assumed. If omitted, will run in unsupervised mode.
X.test	a data frame or matrix (like x) containing predictors for the test set.
Y.test	response for the test set.
coefReg	regularization coefficient chosen for RRF, ranges between 0 and 1.

Value

return a list, including

perf	number of feature selected by RRF, performance (AUC or MSE depending on classification or regression) of RF model using all features, performance (AUC or MSE depending on classification or regression) of RF model using selected features
FullModel	RF model built with all features
ReducedModel	RF model built with only selected features
featureIndex	feature index selected by RRF

Author(s)

Li Liu, Xin Guan

References

Guan, X., & Liu, L. (2018). Know-GRRF: Domain-Knowledge Informed Biomarker Discovery with Random Forests.

Examples

##---- Example: regression ----
library(randomForest)

set.seed(1)
X<-data.frame(matrix(rnorm(100*100), nrow=100))
b=seq(0.1, 2.2, 0.2) 
##y has a linear relationship with first 10 variables
y=b[4]*X$X3+b[5]*X$X4+b[6]*X$X5+b[7]*X$X6+b[8]*X$X7+b[9]*X$X8+b[10]*X$X9+b[11]*X$X10 


##split training and test set
X.train=X[1:70,]
X.test=X[71:100,]
y.train=y[1:70]
y.test=y[71:100]

##use RRF to impute regularized coefficients
imp<-randomForest(X.train, y.train)$importance 
coefReg=imp/max(imp) 

rrf.once(X.train, y.train, X.test, y.test, coefReg)

##---- Example: classification ----
y=as.factor(ifelse(y>0, 1, 0)) ##classification
y.train=y[1:70]
y.test=y[71:100]

##use RRF to impute regularized coefficients
imp<-randomForest(X.train, y.train)$importance 
coefReg=imp/max(imp) 

rrf.once(X.train, y.train, X.test, y.test, coefReg)

KnowGRRF documentation built on May 2, 2019, 6:43 a.m.