cv.xenv: Cross validation for xenv

In therimalaya/envelope: Computing Envelope Estimators in Multivariate Linear Regression

Description

Compute the prediction error for the envelope estimator using m-fold cross validation.

Usage

cv.xenv(X, Y, u, m, nperm)

Arguments

X	Predictors. An n by p matrix, p is the number of predictors and n is number of observations. The predictors must be continuous variables.
Y	Responses. An n by r matrix, r is the number of responses. The response can be univariate or multivariate and must be continuous variable.
u	Dimension of the envelope. An integer between 0 and p.
m	A positive integer that is used to indicate m-fold cross validation.
nperm	A positive integer indicating number of permutations of the observations, m-fold cross validation is run on each permutation.

Details

This function computes prediction errors using m-fold cross validation. For a fixed dimension u, the data is randomly partitioned into m parts, each part is in turn used for testing for the prediction performance while the rest m-1 parts are used for training. This process is repeated for nperm times, and average prediction error is reported. If Y is multivariate, the identity inner product is used for computing the prediction errors.

Value

The output is a real nonnegative number.

cvPE	The prediction error estimated by m-fold cross validation.

Examples

data(wheatprotein)
X <- wheatprotein[, 1:6]
Y <- wheatprotein[, 7]

m <- 5
nperm <- 50
cvPE <- cv.xenv(X, Y, 4, m, nperm)
cvPE

therimalaya/envelope documentation built on May 29, 2019, 1:38 p.m.