EM algorithm for uniform mixtures and t-likelihood
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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. |
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 |
nu |
A positive numeric. The degrees of freedom of the t-likelihood. |
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. |
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. |
lambda |
A vector of numerics greater than or equal to 1, of
length |
print_progress |
A logical. Should we plot the progress? |
print_ziter |
A logical. Should we print the progress of the Newton iterations for updating Z? |
em_z_start_sd |
A positive numeric. Z is initialized by iid normals with this standard deviation and mean 0. |
true_Z |
The true Z values. Used for testing. |
em_tol |
A positive numeric. The stopping criterion for the EM algorithm. |
em_itermax |
A positive integer. The maximum number of iterations to perform on the em step. |
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