glmgam: Fit Gamma Generalized Linear Model by Fisher Scoring with...

In statmod: Statistical Modeling

Fit Gamma Generalized Linear Model by Fisher Scoring with Identity Link

Description

Fit a generalized linear model with secure convergence.

Usage

glmgam.fit(X, y, coef.start = NULL, 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.
coef.start	numeric vector of starting values for the regression coefficients
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.

Details

This function implements 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 equivalent to glm.fit(X,y,family=Gamma(link="identity")) but with much more secure convergence.

Value

List with the following components:

coefficients	numeric vector of regression coefficients
fitted	numeric vector of fitted values
deviance	residual deviance
iter	number of iterations used to convergence. If convergence was not achieved then iter is set to maxit+1.

Author(s)

Gordon Smyth and Yunshun Chen

References

Dunn, PK, and Smyth, GK (2018). Generalized linear models with examples in R. Springer, New York, NY. doi: 10.1007/978-1-4419-0118-7

See Also

glmgam.fit is called by mixedModel2Fit.

glm is the standard glm fitting function in the stats package.

Examples

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

statmod documentation built on Jan. 6, 2023, 5:14 p.m.