unif_grad_llike: My first attempt at calculating the gradient of the...

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

Description

This doesn't seem to work too well.

Usage

unif_grad_llike(pi_Z, lambda, alpha, Y, a_seq, b_seq, sig_diag,
  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.
var_scale	A logical. Should we update the scaling on the variances (TRUE) or not (FALSE).

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