yhat_lars: Fit LASSO Regression using Mallows Cp and Predict

In gencve: General Cross Validation Engine

Description

LASSO regression is fit using the lars algorithm

Usage

yhat_lars(dfTrain, dfTest, normalize = TRUE)

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.
normalize	Default TRUE means the predictors are centered and scaled. Otherwise no transformation.

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_lars(trdf, tedf)

Example output

      69       72       10       68       25        6       55       26 
1.380007 2.073339 1.339053 3.062880 1.811977 0.789856 3.199392 2.073009 
      73       34 
2.665069 1.355079 

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