statmod (version 1.4.26)

glmgam.fit: Fit Generalized Linear Model by Fisher Scoring with Levenberg Damping

Description

Fit a generalized linear model with secure convergence. Provided for gamma glm with identity links or negative binomial glm with log-links.

Usage

glmgam.fit(X, y, coef.start=NULL, tol=1e-6, maxit=50, trace=FALSE)
glmnb.fit(X, y, dispersion, weights=NULL, offset=0, coef.start=NULL, start.method="mean", tol=1e-6, maxit=50, trace=FALSE)

Arguments

X

design matrix, assumed to be of full column rank. Missing values not allowed.

y

numeric vector of responses. Negative or missing values not allowed.

dispersion

numeric vector of over-dispersion parameters for negative binomial. If of length 1, then same over-dispersion is assumed for all observations.

weights

numeric vector of positive weights, defaults to all one.

offset

offset vector for linear model

coef.start

numeric vector of starting values for the regression coefficients

start.method

method used to find starting values, possible values are "mean" and "log(y)"

tol

small positive numeric value giving convergence tolerance

maxit

maximum number of iterations allowed

trace

logical value. If TRUE then output diagnostic information at each iteration.

Value

List with the following components:

Details

These functions implement a modified Fisher scoring algorithm for generalized linear models, similar to the Levenberg-Marquardt algorithm for nonlinear least squares. The Levenberg-Marquardt modification checks for a reduction in the deviance at each step, and avoids the possibility of divergence. The result is a very secure algorithm that converges for almost all datasets.

glmgam.fit is in principle similar to glm.fit(X,y,family=Gamma(link="identity")) but with much more secure convergence. This function is used by mixedModel2Fit.

glmnb.fit is in principle similar to glm.fit(X,y,family=negative.binomial(link="log",theta=1/dispersion)) but with more secure convergence.

Examples

y <- rgamma(10,shape=5)
X <- cbind(1,1:10)
fit <- glmgam.fit(X,y,trace=TRUE)