Description Usage Arguments Details Value See Also

Estimates the responsibilities $z_ij = fracpi_j cdot I_0(y_ijpi_j cdot I_0(y_ij + (1-pi_j) cdot f_count(y_ij

1 | ```
doEStep(countResiduals, zeroResiduals, zeroIndices)
``` |

`countResiduals` |
Residuals from the count model. |

`zeroResiduals` |
Residuals from the zero model. |

`zeroIndices` |
Index (matrix m x n) of counts that are zero/non-zero. |

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 $z_ij = pr(delta_ij=1 | data)$.

Updated matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).

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