normal_llike_grad: Gradient of the log-likelihood wrt Z.

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

Description

Gradient of the log-likelihood wrt Z.

Usage

normal_llike_grad(Z, Y, alpha, sig_diag, tau_seq, scale_val, pi_vals,
  lambda = NULL)

Arguments

Z	A k by 1 matrix of numerics. The hidden confounders.
Y	A matrix of dimension p by 1. These are the observed regression coefficients of the observed variables.
alpha	A matrix. This is of dimension p by k and are the coefficients to the confounding variables.
sig_diag	A vector of length p containing the variances of the observations.
tau_seq	A vector of length M containing the standard deviations (not variances) of the mixing distributions.
scale_val	A positive numeric. The variance scaling parameter.
pi_vals	A vector of numerics that sums to 1. The mixing proportions.
lambda	Not used here, but needed for optim.

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