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mixedLSR

Mixed, low-rank, and sparse multivariate regression (mixedLSR) provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. mixedLSR allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix.

Installation

You can install the development version of mixedLSR from GitHub with:

# install.packages("devtools")
devtools::install_github("alexanderjwhite/mixedLSR")

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Version

Install

install.packages('mixedLSR')

Monthly Downloads

171

Version

0.1.0

License

MIT + file LICENSE

Maintainer

Alexander White

Last Published

November 4th, 2022

Functions in mixedLSR (0.1.0)

fct_sim_anneal

Internal Simulated Annealing Function
fct_new_assign

Internal Perturb Function
bic_lsr

Compute Bayesian information criterion for a mixedLSR model
fct_dpp

Internal Double Penalized Projection Function
fct_select_lambda

Internal Penalty Parameter Selection Function.
fct_rank

Internal Rank Estimation Function
fct_em

Internal EM Algorithm
fct_log_lik

Internal Log-Likelihood Function
fct_j_lik

Internal Likelihood Function
fct_pi_vec

Internal Pi Function
mixed_lsr

Mixed Low-Rank and Sparse Multivariate Regression for High-Dimensional Data
fct_gamma

Internal Posterior Calculation
fct_sigma

Internal Sigma Estimation Function
simulate_lsr

Simulate Heterogeneous, Low-Rank, and Sparse Data
fct_alt_optimize

Internal Alternating Optimization Function
fct_weighted_ll

Internal Weighted Log Likelihood Function
plot_lsr

Heatmap Plot of the mixedLSR Coefficient Matrices
fct_initialize

Internal Partition Initialization Function