unif_grad_simp: This is me being very careful about calculating the gradient...

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

Description

This is me being very careful about calculating the gradient of the likelihood function wrt Z.

Usage

unif_grad_simp(pi_Z, lambda, alpha, Y, sig_diag, left_seq = NULL,
  right_seq = NULL, a_seq = NULL, b_seq = NULL, var_scale = TRUE,
  likelihood = c("normal", "t"), df = NULL)

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.
sig_diag	A vector of length p containing the variances of the observations.
left_seq	The left endpoints of the uniforms.
right_seq	The right endpoints of the uniforms
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.
var_scale	A logical. Should we update the scaling on the variances (TRUE) or not (FALSE).
likelihood	Can be "normal" or "t".
df	A positive numeric. The degrees of freedom if the likelihood is t.

Value

The gradient for Z.

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