Cross validation for eenvlp
cv_eenvlp.RdCompute the prediction error for the enhanced response envelope estimator using 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.
- lamb
A regularization parameter. A positive value.
- 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.
- index
Either NULL or an n vector that is a list of indices from 1 to n. If NULL (the default), then the samples are ordered randomly and split. If a vector is provided, then the samples are ordered accroding to the given list of indices and split.
- X.scale
A boolean. If TRUE (the default), then the columns of training X are divided by their standard deviations. If False, no scaling is done.
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(cereal)
cvPE <- cv_eenvlp(cereal$x, cereal$y, u=1, lamb=1e-07, m=2, nperm=1)
cvPE
#> [1] 0.9505697