gencve (version 0.3)

yhat_gel: Elastic Net Regression Prediction

Description

Fit regression using 10-fold CV with the 1 standard deviation rule and compute predictions.

Usage

yhat_gel(dfTrain, dfTest, alpha = 1)

Arguments

dfTrain

Data frame for training data. Last column must be the output variable.

dfTest

Data frame for test data. Last column must be the output variable.

alpha

Must be in [0,1], alpha=1 for LASSO (default), alpha=0 for ridge regression. Another recommended choice is alpha=0.5.

Value

Examples

Xy <- prostate
X <- prostate[,-9]
y <- prostate[,9]
n <- length(y)
d <- 10
set.seed(777513)
iTe <- sample(n, size=d)
iTr <- (1:n)[!match(1:n, iTe, nomatch = 0) > 0]
trdf <- data.frame(X[iTr,], y=y[iTr]) #X, y already defined
tedf <- data.frame(X[iTe,], y=y[iTe])
yhat_gel(trdf, tedf)