getNegativeLogLikelihoods

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

countResiduals

zeroResiduals


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 $z_ij = pr(delta_ij=1 | data and current values)$.


metagenomeSeq is designed to determine features (be it
Operational Taxanomic Unit (OTU), species, etc.) that are
differentially abundant between two or more groups of multiple
samples. metagenomeSeq is designed to address the effects of
both normalization and under-sampling of microbial communities
on disease association detection and the testing of feature
correlations.

getNegativeLogLikelihoods: Calculate the negative log-likelihoods for the various features given the residuals.

Description

Usage

Arguments

Value

See Also