fct_gamma: Internal Posterior Calculation

In mixedLSR: Mixed, Low-Rank, and Sparse Multivariate Regression on High-Dimensional Data

Internal Posterior Calculation

Description

Internal Posterior Calculation

Usage

fct_gamma(
  x,
  y,
  k,
  N,
  clust_assign,
  pi_vec,
  lambda,
  alpha,
  beta,
  y_sparse,
  rank,
  max_rank
)

Arguments

x	A matrix of predictors.
y	A matrix of responses.
k	The number of groups.
N	The sample size.
clust_assign	The current clustering assignment.
pi_vec	A vector of mixing probabilities for each cluster label.
lambda	A vector of penalization parameters.
alpha	A positive constant DPP parameter.
beta	A positive constant DPP parameter.
y_sparse	Should Y coefficients be treated as sparse?
rank	The rank, if known.
max_rank	The maximum allowed rank.

Value

A list with the posterior, coefficients, and estimated covariance.

mixedLSR documentation built on Nov. 10, 2022, 6:17 p.m.