write.roc: write test ROC to a data table.

In KnowGRRF: Knowledge-Based Guided Regularized Random Forest

Description

write a data table including False Positive Rate, True Positive Rate and cutoff on test dataset. Work for classification only.

Usage

write.roc(X.train, Y.train, X.test, Y.test, fea, file.name="")

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.
fea	feature index or feature names used to train the model.
file.name	directory and name of files that write to. If directory is not given, will write to working directory.

Value

a data table in csv format which columns FPR, TPR and cutoff.

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 ----

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[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]

##save to a temp file
write.roc(X.train, y.train, X.test, y.test, fea=1:20, paste(tempdir(), "example.csv", sep="/"))  

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