cv.rrenv.apweights: Cross validation for rrenv.apweights

View source: R/cv.rrenv.apweights.R

cv.rrenv.apweightsR Documentation

Cross validation for rrenv.apweights


Compute the prediction error using m-fold cross validation for the reduced rank envelope estimator that accommodates nonconstant error covariance.


cv.rrenv.apweights(X, Y, u, d, 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.


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


The rank of the coefficient matrix. An integer between 0 and u.


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 <- vehicles[, 1:11]  
Y <- vehicles[, 12:15]
X <- scale(X)
Y <- scale(Y)  # The scales of Y are vastly different, so scaling is reasonable here

m <- 5
nperm <- 50
## Not run: cvPE <- cv.rrenv.apweights(X, Y, 3, 2, m, nperm)
## Not run: cvPE

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