Ridge Regression.

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Description

This function computes the optimal ridge regression model based on cross-validation.

Usage

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ridge.cv(X, y, lambda, scale = TRUE, k = 10, plot.it = FALSE)

Arguments

X

matrix of input observations. The rows of X contain the samples, the columns of X contain the observed variables

y

vector of responses. The length of y must equal the number of rows of X

lambda

Vector of penalty terms.

scale

Scale the columns of X? Default is scale=TRUE.

k

Number of splits in k-fold cross-validation. Default value is k=10.

plot.it

Plot the cross-validation error as a function of lambda? Default is FALSE.

Value

intercept

cross-validation optimal intercept

coefficients

cross-validation optimal regression coefficients

lambda.opt

optimal value of lambda.

Author(s)

Nicole Kraemer

See Also

ridge.net

Examples

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n<-100 # number of observations
p<-60 # number of variables
X<-matrix(rnorm(n*p),ncol=p) 
y<-rnorm(n)
ridge.object<-ridge.cv(X,y)