Renvlp (version 2.7)

cv.genv: Cross validation for genv

Description

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

Usage

cv.genv(X, Y, Z, u, m, nperm)

Arguments

X

Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.

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.

Z

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

u

Dimension of the groupwise 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(fiberpaper)
X <- fiberpaper[ , c(5, 7)]
Y <- fiberpaper[ , 1:3]
Z <- as.numeric(fiberpaper[ , 6] > mean(fiberpaper[ , 6]))

# }
# NOT RUN {
m <- 5
# }
# NOT RUN {
nperm <- 50
# }
# NOT RUN {
cvPE <- cv.genv(X, Y, Z, 2, m, nperm)
# }
# NOT RUN {
cvPE
# }
# NOT RUN {
# }