ultimate_deconvolution: EM algorithm to fit multivariate Guassian convolution...

In dcgerard/UltimateDeconvolution: Deconvolution Problem in Multivariate Gaussian Mixtures

Description

EM algorithm to fit multivariate Guassian convolution problem.

Usage

ultimate_deconvolution(x_mat, s_mat, v_mat, pi_vec, itermax = 500,
  tol = 10^-5, plot_iter = FALSE, code = c("cpp", "R"))

Arguments

x_mat	A matrix of data. The rows index the observations and the columns index the variables.
s_mat	A matrix of variances (NOT standard deviations). The rows index the observations and the columns index the variables.
v_mat	A matrix of initial values for the low-rank mixture covariances.
pi_vec	A vector of initial values of the mixing proportions.
itermax	The maximum number of fixed-point iterations for the EM to run.
tol	The tolerance for the stopping criterion. The current stopping criterion is the ratio of successive likelihoods minus 1.
plot_iter	A logical. Should we plot the likelihood at each step (TRUE) or not (FALSE)?
code	Should we use the C++ code ("cpp") or the R code ("R")?

Author(s)

David Gerard

dcgerard/UltimateDeconvolution documentation built on May 15, 2019, 1:24 a.m.