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)

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.

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.

Author

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

Run this code

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)

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