Index (matrix m x n) of counts that are zero/non-zero.
Value
Updated matrix (m x n) of estimate responsibilities (probabilities
that a count comes from a spike distribution at 0).
Details
Maximum-likelihood estimates are approximated using the EM algorithm where
we treat mixture membership $delta_ij$ = 1 if $y_ij$ is generated from the
zero point mass as latent indicator variables. The density is defined as
$f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdot
f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is
$(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log
pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined
as $z_ij = pr(delta_ij=1 | data)$.