Calculate the negative log-likelihoods for the various features given the residuals.
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 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 $z_ij = pr(delta_ij=1 | data and current values)$.
getNegativeLogLikelihoods(z, countResiduals, zeroResiduals)
Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
Residuals from the count model.
Residuals from the zero model.
Vector of size M of the negative log-likelihoods for the various features.
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