cv.sxenv: Cross validation for sxenv

cv.sxenvR Documentation

Cross validation for sxenv

Description

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

Usage

cv.sxenv(X, Y, u, R, 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 scaled envelope. An integer between 0 and r.

R

The number of replications of the scales. A vector, the sum of all elements of R must be 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. As 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(sales)
Y <- sales[, 1:3]
X <- sales[, 4:7]
R <- rep(1, 4)

m <- 5
nperm <- 50
## Not run: cvPE <- cv.sxenv(X, Y, 2, R, m, nperm)
## Not run: cvPE

Renvlp documentation built on Aug. 8, 2022, 1:06 a.m.

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