irls.nb.1: Estimate the regression coefficients in an NB GLM model

In NBPSeq: Negative Binomial Models for RNA-Sequencing Data

Estimate the regression coefficients in an NB GLM model

Description

Estimate the regression coefficients in an NB GLM model with known dispersion parameters

Usage

irls.nb.1(y, s, x, phi, beta0 = rep(NA, p), mustart = NULL, maxit = 50,
  tol.mu = 0.001/length(y), print.level = 0)

Arguments

y	an n vector of counts
s	a scalar or an n vector of effective library sizes
x	an n by p design matrix
phi	a scalar or an n-vector of dispersion parameters
mustart	starting values for the vector of means
beta0	a vector specifying known and unknown components of the regression coefficients: non-NA components are hypothesized values of beta, NA components are free components
maxit	maximum number of iterations
tol.mu	a number, convergence criteria
print.level	a number, print level

Details

This function estimates the regression coefficients using iterative reweighted least squares (IRLS) algorithm, which is equivalent to Fisher scoring. The implementation is based on glm.fit.

Users can choose to fix some regression coefficients by specifying beta0. (This is useful when fitting a model under a null hypothesis.)

Value

a list of the following components:

beta	a p-vector of estimated regression coefficients
mu	an n-vector of estimated mean values
conv	logical. Was the IRLS algorithm judged to have converged?
zero	logical. Was any of the fitted mean close to 0?

NBPSeq documentation built on June 9, 2022, 5:06 p.m.