t_unif_em: EM algorithm for uniform mixtures and t-likelihood

In dcgerard/succotashr: Surrogate and Confounder Correction Occuring Together with Adaptive Shrinkage

Description

EM algorithm for uniform mixtures and t-likelihood

Usage

t_unif_em(a_seq, b_seq, Y, alpha, sig_diag, nu, pi_init, Z_init, pi_init_type,
  lambda, print_progress, print_ziter, em_z_start_sd, true_Z = NULL,
  em_tol = 10^-6, em_itermax = 200)

Arguments

a_seq	A vector of negative numerics in increasing order. The negative end points in an [a, 0] grid.
b_seq	A vector of positive numerics in increasing order. The positive end points in a [0, b] grid.
Y	A p by 1 matrix of numerics. The data.
alpha	A p by k matrix of numerics. The confounder coefficients.
sig_diag	A vector of the variances of Y.
nu	A positive numeric. The degrees of freedom of the t-likelihood.
pi_init	A vector of non-negative numerics that sum of 1 of length length(a) + length(b) + 1. The initial values of the mixture probs.
Z_init	A vector of length k of numerics. Starting values of Z.
pi_init_type	Either "random", "uniform", or "zero_conc". How should we choose the initial mixture probabilities if pi_init is NULL? "random" will draw draw pi uniformly from the simplex. "uniform" will give each value equal mass. "zero_conc" will give more mass to 0 than any other probability.
lambda	A vector of numerics greater than or equal to 1, of length length(a) + length(b) + 1.
print_progress	A logical. Should we plot the progress?
print_ziter	A logical. Should we print the progress of the Newton iterations for updating Z?
em_z_start_sd	A positive numeric. Z is initialized by iid normals with this standard deviation and mean 0.
true_Z	The true Z values. Used for testing.
em_tol	A positive numeric. The stopping criterion for the EM algorithm.
em_itermax	A positive integer. The maximum number of iterations to perform on the em step.

dcgerard/succotashr documentation built on May 15, 2019, 1:25 a.m.