tsmvr_cv: k-fold cross-validation for tsmvr

In spcorum/tsmvr-saved: Truly Sparse Multivariate Regression

Description

Calculates the mean and standard deviation of errors over a tsmvr k-fold cross-validation experiment. The error for each base model is the normalized squared error between the true response and the predicted response on a given cross-validation set.

Usage

tsmvr_cv(X, Y, s1, s2, pars, quiet = F, seed = NULL)

Arguments

X	design matrix (n-by-p)
Y	response matrix (n-by-q)
s1	regressor matrix sparsity (positive integer)
s2	covariance matrix sparsity (positive integer)
pars	list of algorithm parameters; output of set_parameters
quiet	(logical)
seed	set random seed (integer)
k	number of k-folds (integer greater than 1)

Value

A list of the mean and standard deviation of the errors across the K folds.

Note

See also squared_error, k_folds, tsmvr_solve, and set_parameters.

spcorum/tsmvr-saved documentation built on Nov. 5, 2019, 9:15 a.m.