Estimates the clusters and provides the coefficients for an mcen object
mcen_workhorse(beta, delta = NULL, xx, xy, family = "mgaussian",
ky = NULL, gamma_y = 0.5, eps = 1e-05, clusterMethod = "kmeans",
clusterIterations = 100, clusterStartNum = 30, cluster_y = NULL,
max_iter = 10, x = x)The initial value of the coefficients
The sparsity (L1) tuning parameter
Matrix of transpose of x times x.
Matrix of transpose of x times y.
Type of likelihood used two options "mgaussian" or "mbinomial"
Number of clusters for the response
Penalty for the y clusters difference in predicted values
Convergence criteria
Which clustering method was used, currently support kmeans or kmeanspp
Number of iterations for cluster convergence
Number of random starting points for clustering
An a priori definition of clusters. If clusters are provided they will remain fixed and are not estimated. Objective function is then convex.
The maximum number of iterations for estimating the coefficients
The design matrix
Ben Sherwood <ben.sherwood@ku.edu>, Brad Price <brad.price@mail.wvu.edu>