fit_succotash_unif_coord: Coordinate ascent algorithm for normal likelihood and...

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

Description

Coordinate ascent algorithm for normal likelihood and mixtures of uniforms.

Usage

fit_succotash_unif_coord(pi_Z, lambda, alpha, Y, a_seq, b_seq, sig_diag,
  print_ziter = FALSE, newt_itermax = 100, tol = 10^-4,
  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 p by 1 matrix of numerics. The data.
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 the variances of Y.
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).
likelihood	Can be "normal" or "t".
df	A positive numeric. The degrees of freedom if the likelihood is t.

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