| cv.mcen {mcen} | R Documentation |
Cross validation for mcen function
Description
Cross validation for mcen function
Usage
cv.mcen(x, y, family = "mgaussian", ky = seq(2, 4), gamma_y = seq(0.1,
5.1, 0.5), nfolds = 10, folds = NULL, cluster_y = NULL, delta=NULL, n.cores = 1,
...)
Arguments
x |
Matrix set of predictors. |
y |
Matrix set of responses. |
family |
The exponential family the response corresponds to. |
ky |
A vector with the number of possible clusters for y. |
gamma_y |
Set of tuning parameter for clustering penalty in response categories. |
nfolds |
Number of folds used in the cross-validation. |
folds |
A vector of length n, where this identifies what fold of the kfold cross validation each observation belongs to. |
cluster_y |
a priori definition of clusters. If clusters are provided they will remain fixed and are not estimated. Objective function is then convex. |
delta |
Tuning parameter for the L1 penalty |
n.cores |
Number of cores used for parallel processing. |
... |
The variables passed to mcen |
Value
Returns a cv.mcen object.
models |
A list of mcen objects. |
cv |
Cross validation results. |
ky |
The same value as the input ky. |
gamma_y |
The same value as the input gamma_y. |
Author(s)
Ben Sherwood <ben.sherwood@ku.edu>, Brad Price <brad.price@mail.wvu.edu>
References
Price, B.S. and Sherwood, B. (2018). A Cluster Elastic Net for Multivariate Regression. arXiv preprint arXiv:1707.03530. http://arxiv-export-lb.library.cornell.edu/abs/1707.03530.
Examples
x <- matrix(rnorm(400),ncol=4)
beta <- beta <- matrix(c(1,1,0,0,0,0,-1,-1,0,0,-1,-1,1,1,0,0),ncol=4)
y <- x%*%beta + rnorm(400)
cv_fit <- cv.mcen(x,y,ky=2)