Renvlp (version 2.7)

cv.henv: Cross validation for henv

Description

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

Usage

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

Arguments

X

A group indicator vector of length \(n\), where \(n\) denotes the number of observations.

Y

Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.

u

Dimension of the heteroscedastic envelope. An integer between 0 and r.

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.

Value

The output is a real nonnegative number.

cvPE

The prediction error estimated by m-fold cross validation.

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. As Y is multivariate, the identity inner product is used for computing the prediction errors.

Examples

# NOT RUN {
data(waterstrider)
X <- waterstrider[ , 1]
Y <- waterstrider[ , 2:5]

m <- 5
nperm <- 50

# }
# NOT RUN {
cvPE <- cv.henv(X, Y, 2, m, nperm)
# }
# NOT RUN {
cvPE
# }