cv.penv: Cross validation for penv

View source: R/cv.penv.R

cv.penvR Documentation

Cross validation for penv


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


cv.penv(X1, X2, Y, u, m, nperm)



Predictors of main interest. An n by p1 matrix, n is the number of observations, and p1 is the number of main predictors. The predictors can be univariate or multivariate, discrete or continuous.


Covariates, or predictors not of main interest. An n by p2 matrix, p2 is the number of covariates.


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.


Dimension of the 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.


X1 <- fiberpaper[, 7]
X2 <- fiberpaper[, 5:6]
Y <- fiberpaper[, 1:4]

m <- 5
nperm <- 50
## Not run: cvPE <- cv.penv(X1, X2, Y, 1, m, nperm)
## Not run: cvPE

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

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