doCountMStep: Compute the Maximization step calculation for features still...

In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Description

Maximization step is solved by weighted least squares. The function also computes counts residuals.

Usage

doCountMStep(z, y, mmCount, stillActive, fit2 = NULL, dfMethod = "modified")

Arguments

z	Matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spike distribution at 0).
y	Matrix (m x n) of count observations.
mmCount	Model matrix for the count distribution.
stillActive	Boolean vector of size M, indicating whether a feature converged or not.
fit2	Previous fit of the count model.
dfMethod	Either 'default' or 'modified' (by responsibilities)

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 $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * f_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (s_j))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$.

Value

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

See Also

fitZig

metagenomeSeq documentation built on Nov. 8, 2020, 5:34 p.m.