mixed_lsr {mixedLSR}

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

Description

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

Usage

mixed_lsr(
  x,
  y,
  k,
  nstart = 1,
  init_assign = NULL,
  init_lambda = NULL,
  alt_iter = 5,
  anneal_iter = 1000,
  em_iter = 1000,
  temp = 1000,
  mu = 0.95,
  eps = 1e-06,
  accept_prob = 0.95,
  sim_N = 200,
  verbose = TRUE
)

Arguments

x

A matrix of predictors.

y

A matrix of responses.

k

The number of groups.

nstart

The number of random initializations, the result with the maximum likelihood is returned.

init_assign

A vector of initial assignments, NULL by default.

init_lambda

A vector with the values to initialize the penalization parameter for each group, e.g., c(1,1,1). Set to NULL by default.

alt_iter

The maximum number of times to alternate between the classification expectation maximization algorithm and the simulated annealing algorithm.

anneal_iter

The maximum number of simulated annealing iterations.

em_iter

The maximum number of EM iterations.

temp

The initial simulated annealing temperature, temp > 0.

mu

The simulated annealing decrease temperature fraction. Once the best configuration cannot be improved, reduce the temperature to (mu)T, 0 < mu < 1.

eps

The final simulated annealing temperature, eps > 0.

accept_prob

The simulated annealing probability of accepting a new assignment 0 < accept_prob < 1. When closer to 1, trial assignments will only be small perturbation of the current assignment. When closer to 0, trial assignments are closer to random.

sim_N

The simulated annealing number of iterations for reaching equilibrium.

verbose

A boolean indicating whether to print to screen.

Value

A list containing the likelihood, the partition, the coefficient matrices, and the BIC.

Examples

simulate <- simulate_lsr(50)
mixed_lsr(simulate$x, simulate$y, k = 2, init_lambda = c(1,1), alt_iter = 0)