constrained.loglikelihood: Define the objective function in optimization procedure for...

In ZIBBSeqDiscovery: Zero-Inflated Beta-Binomial Modeling of Microbiome Count Data

Description

The objective function is the negative of log likelihood function.

Usage

constrained.loglikelihood(para, X, Y.col, coeff, Y.c, ziMatrix)

Arguments

para	Vector of optimized parameters with length p+q, where p is the number of covariates for count model (e.g., beta-binomial), q is the number of covariates for zero model. The first p elements are betas which are the effects/coefficients for the count model. The last q elements are etas which are the effects/coefficients for the zero model.
X	The design matrix (n by p, p is the number of covariates) for the count model (e.g., beta-binomial), and intercept is included.
Y.col	Vector of counts corresponding to an OTU, with length n.
coeff	Vector of coefficients in the polynomial mean-overdispersion relationship in constrained approach.
Y.c	Vector of library size with length n.
ziMatrix	The design matrix (n by q) for the zero model, and intercept is included.

Author(s)

Tao Hu, Yihui Zhou

ZIBBSeqDiscovery documentation built on May 2, 2019, 4:21 a.m.