mcen {mcen}

Fits an MCEN model

Description

Fits an MCEN model

Usage

mcen(x, y, family = "mgaussian", ky = NULL, delta = NULL, gamma_y = 1,
  ndelta = 25, delta.min.ratio = NULL, eps = 1e-05,
  scale_x = TRUE, scale_y = TRUE, clusterMethod = "kmeans",
  clusterStartNum = 30, clusterIterations = 10, cluster_y = NULL,
  max_iter = 10, init_beta = NULL, n.cores = 1)

Arguments

x

Matrix of predictors.

y

Matrix of responses.

family

Type of likelihood used two options "mgaussian" or "mbinomial".

ky

Clusters for response.

delta

L1 penalty.

gamma_y

Penalty for with y clusters difference in predicted values.

ndelta

Number of delta parameters.

delta.min.ratio

Ratio between smallest and largest delta.

eps

Convergence criteria.

scale_x

Whether x matrix should be scaled, default is True.

scale_y

Whether y matrix should be scaled, default is True.

clusterMethod

K-means function used kmeans or kmeanspp.

clusterStartNum

Number of random starting points for clustering.

clusterIterations

Number of iterations for cluster convergence.

cluster_y

An a priori definition of clusters. If clusters are provided they will remain fixed and are not estimated. Objective function is then convex.

max_iter

Maximum number of iterations for coefficient estimates.

init_beta

Clustering step requires an initial estimate, default is to use elastic net solution.

n.cores

Number of cores used for calculation default is 1.

Value

returns a MCEN object

beta

List of the coefficient estimates.

delta

Value of delta.

gamma_y

Value of gamma_y.

ky

Value of ky.

y_clusters

List of the clusters of y.

Author(s)

Ben Sherwood <[e-mail]>, Brad Price <[e-mail]>

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) 
mcen_fit <- mcen(x,y,ky=2,delta=1)