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

In gu-mi/NBGOF: Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data

Description

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

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

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), maxit = 50,
  tol.mu = 0.001/length(y), print.level = 0)

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

irls.nb.1(y, s, x, phi, beta0 = rep(NA, p), 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	a n by p design matrix
phi	a scalar or an n-vector of dispersion parameters
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
tol.mu	convergence criteria
print.level
y	an n vector of counts
s	a scalar or an n vector of effective library sizes
x	a n by p design matrix
phi	a scalar or an n-vector of dispersion parameters
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
tol.mu	convergence criteria
print.level
y	an n vector of counts
s	a scalar or an n vector of effective library sizes
x	a n by p design matrix
phi	a scalar or an n-vector of dispersion parameters
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
tol.mu	convergence criteria
print.level

Details

This function estimate <beta> using iterative reweighted least squares (IRLS) algorithm, which is equivalent to Fisher scoring. We used the glm.fit code as a template.

This function estimate <beta> using iterative reweighted least squares (IRLS) algorithm, which is equivalent to Fisher scoring. We used the glm.fit code as a template.

This function estimate <beta> using iterative reweighted least squares (IRLS) algorithm, which is equivalent to Fisher scoring. We used the glm.fit code as a template.

Value

a list of the following components: beta, a p-vector of estimated regression coefficients mu, an n-vector of estimated mean values converged, logical. Was the IRLS algorithm judged to have converged? @useDynLib NBGOF Cdqrls @keywords internal

a list of the following components: beta, a p-vector of estimated regression coefficients mu, an n-vector of estimated mean values converged, logical. Was the IRLS algorithm judged to have converged? @useDynLib NBGOF Cdqrls @keywords internal

a list of the following components: beta, a p-vector of estimated regression coefficients mu, an n-vector of estimated mean values converged, logical. Was the IRLS algorithm judged to have converged? @useDynLib NBGOF Cdqrls @keywords internal

gu-mi/NBGOF documentation built on Oct. 25, 2020, 3:30 a.m.