EM algorithm for second step of SUCCOTASH
1 2 3 4 5 6 7 | uniform_succ_em(Y, alpha, sig_diag, a_seq, b_seq, pi_init = NULL,
Z_init = NULL, lambda = NULL, print_ziter = FALSE,
print_progress = FALSE, em_z_start_sd = 1, em_itermax = 200,
em_tol = 10^-3, pi_init_type = c("random", "uniform", "zero_conc"),
true_Z = NULL, var_scale = TRUE, optmethod = c("coord", "em"),
likelihood = c("normal", "t"), df = NULL, z_init_type = c("null_mle",
"random"), var_scale_init_type = c("null_mle", "one", "random"))
|
Y |
A p by 1 matrix of numerics. The data. |
alpha |
A p by k matrix of numerics. The confounder coefficients. |
sig_diag |
A vector of the variances of |
a_seq |
A vector of negative numerics in increasing order. The negative end points in an [a, 0] grid. |
b_seq |
A vector of positive numerics in increasing order. The positive end points in a [0, b] grid. |
pi_init |
A vector of non-negative numerics that sum of 1 of
length |
Z_init |
A vector of length k of numerics. Starting values of Z. |
lambda |
A vector of numerics greater than or equal to 1, of
length |
print_ziter |
A logical. Should we print the progress of the Newton iterations for updating Z? |
print_progress |
A logical. Should we plot the progress? |
em_z_start_sd |
A positive numeric. Z is initialized by iid normals with this standard deviation and mean 0. |
em_itermax |
A positive integer. The maximum number of iterations to perform on the em step. |
em_tol |
A positive numeric. The stopping criterion for the EM algorithm. |
pi_init_type |
Either "random", "uniform", or "zero_conc". How should we choose the initial mixture probabilities if pi_init is NULL? "random" will draw draw pi uniformly from the simplex. "uniform" will give each value equal mass. "zero_conc" will give more mass to 0 than any other probability. |
true_Z |
The true Z values. Used for testing. |
var_scale |
A logical. Should we update the scaling on the
variances ( |
optmethod |
Either coordinate ascent ( |
likelihood |
Which likelihood should we use? Normal
( |
df |
The degrees of freedom of the the t-likelihood if
|
z_init_type |
How should we initiate the confounders? At the
all-null MLE ( |
var_scale_init_type |
If |
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