t_unif_em: EM algorithm for uniform mixtures and t-likelihood

Description Usage Arguments

Description

EM algorithm for uniform mixtures and t-likelihood

Usage

1
2
3
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.