cv.genv: Cross validation for genv

View source: R/cv.genv.R

cv.genvR Documentation

Cross validation for genv


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


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



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


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.


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


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


A positive integer that is used to indicate m-fold cross validation.


A positive integer indicating number of permutations of the observations, m-fold cross validation is run on each permutation.


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.


The output is a real nonnegative number.


The prediction error estimated by m-fold cross validation.


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

Renvlp documentation built on Oct. 11, 2023, 1:06 a.m.

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