rf.once: Build random forest once and return AUC for both training and...

In KnowGRRF: Knowledge-Based Guided Regularized Random Forest

Description

Build a random forest model once and return AUC for both training prediction (out-of-bag predictions) and test prediction. Work for classification only.

Usage

rf.once(X.train, Y.train, X.test, Y.test, fea)

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	an optional data frame or matrix (like x) containing predictors for the test set.
Y.test	optional response for the test set.
fea	feature index or feature names used to train the model.

Value

return a list, including

AUC	AUC calculated from out-of-bag prediction from random forest classification model
Test.AUC	AUC calculated from test prediction from random forest classification model. Only available when test set is given

Author(s)

Li Liu, Xin Guan

References

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

Examples

##---- Example: classification ----
library(randomForest)
library(PRROC)
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[6]*X$X5+b[7]*X$X6+b[8]*X$X7+b[9]*X$X8+b[10]*X$X9+b[11]*X$X10 
y=as.factor(ifelse(y>0, 1, 0)) ##classification

##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]

rf.once(X.train, y.train, fea=1:20)  ##no test set
rf.once(X.train, y.train, X.test, y.test, 1:10)  ##relevant feature set
rf.once(X.train, y.train, X.test, y.test, 11:20)  ##irrelevant feature set

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