R: Ridge Regression.

ridge.cv {plsdof}

R Documentation

Ridge Regression.

Description

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

Usage

ridge.cv(X, y, lambda, scale = TRUE, k = 10, plot.it = FALSE,
       groups=NULL,method.cor="pearson",compute.jackknife)

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.
`groups`	an optional vector with the same length as `y`. It encodes a partitioning of the data into distinct subgroups. If `groups` is provided, `k=10` is ignored and instead, cross-validation is performed based on the partioning. Default is `NULL`.
`method.cor`	How should the correlation to the response be computed? Default is ”pearson”.
`compute.jackknife`	Logical. If `TRUE`, the regression coefficients on each of the cross-validation splits is stored. Default is `TRUE`.

Details

Based on the regression coefficients coefficients.jackknife computed on the cross-validation splits, we can estimate their mean and their variance using the jackknife. We remark that under a fixed design and the assumption of normally distributed y-values, we can also derive the true distribution of the regression coefficients.

Value

`cv.error.matrix`	matrix of cross-validated errors based on mean squared error. A row corresponds to one cross-validation split.
`cv.error`	vector of cross-validated errors based on mean squared error
`lambda.opt`	optimal value of `lambda`, based on mean squared error
`intercept`	intercept of the optimal model, based on mean squared error
`coefficients`	vector of regression coefficients of the optimal model, based on mean squared error
`cor.error.matrix`	matrix of cross-validated errors based on correlation. A row corresponds to one cross-validation split.
`cor.error`	vector of cross-validated errors based on correlation
`lambda.opt.cor`	optimal value of `lambda`, based on correlation
`intercept.cor`	intercept of the optimal model, based on correlation
`coefficients.cor`	vector of regression coefficients of the optimal model, based on mean squared error
`coefficients.jackknife`	Array of the regression coefficients on each of the cross-validation splits. The dimension is `ncol(X) x length(lambda) x k`.

Author(s)

Nicole Kraemer

Examples

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)

[Package plsdof version 0.2-7 Index]