yhat_gel: Elastic Net Regression Prediction

In gencve: General Cross Validation Engine

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

The predictions for the test sample

Author(s)

A. I. McLeod

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)

gencve documentation built on May 2, 2019, 6:08 a.m.