succotash_unif_fixed: A fixed point iteration in the mixture of uniforms EM.

Description Usage Arguments Value See Also

Description

This is a fixed-point iteration for the SUCCOTASH EM algorithm. This updates the estimate of the prior and the estimate of the hidden covariates.

Usage

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succotash_unif_fixed(pi_Z, lambda, alpha, Y, a_seq, b_seq, sig_diag,
  print_ziter = FALSE, newt_itermax = 4, tol = 10^-4, var_scale = TRUE)

Arguments

pi_Z

A vector. The first M values are the current values of π. The last k values are the current values of Z.

lambda

A vector. This is a length M vector with the regularization parameters for the mixing proportions.

alpha

A matrix. This is of dimension p by k and are the coefficients to the confounding variables.

Y

A matrix of dimension p by 1. These are the observed regression coefficients of the observed variables.

a_seq

A vector of negative numerics containing the left endpoints of the mixing uniforms.

b_seq

A vector of positiv numerics containing the right endpoints of the mixing uniforms.

sig_diag

A vector of length p containing the variances of the observations.

print_ziter

A logical. Should we we print each iteration of the Z optimization?

newt_itermax

A positive integer. The maximum number of Newton steps to perform in updating Z.

tol

A positive numeric. The stopping criterion for Newton's method in updating Z.

var_scale

A logical. Should we update the scaling on the variances (TRUE) or not (FALSE).

Value

pi_new A vector of length M. The update for the mixing components.

Z_new A vector of length k. The update for the confounder covariates.

See Also

uniform_succ_given_alpha succotash_llike_unif


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