Description Usage Arguments Value See Also
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.
1 2 | t_succotash_unif_fixed(pi_Z, lambda, alpha, Y, nu, a_seq, b_seq, sig_diag,
print_ziter = TRUE, newt_itermax = 10, tol = 10^-4)
|
pi_Z |
A vector. The first |
lambda |
A vector. This is a length |
alpha |
A matrix. This is of dimension |
Y |
A matrix of dimension |
nu |
A positive numeric. The degrees of freedom of the t-distribution. |
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 |
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. |
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.
t_uniform_succ_given_alpha
t_succotash_llike_unif
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