cv.env.apweights: Cross validation for env.apweights
Description
Compute the prediction error using m-fold cross validation for the response envelope estimator that accommodates nonconstant variance.
Usage
cv.env.apweights(X, Y, u, m, nperm)
Value
The output is a real nonnegative number.
cvPE
The prediction error estimated by m-fold cross validation.
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.
u
Dimension of the 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.
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.
data(concrete)
X <- concrete[, 1:7]
Y <- concrete[, 8:10]
if (FALSE) u <- u.env.apweights(X, Y)
if (FALSE) u
m <- 5
nperm <- 50
if (FALSE) cvPE <- cv.env.apweights(X, Y, 2, m, nperm)
if (FALSE) cvPE