Fit Gamma Generalized Linear Model by Fisher Scoring with Identity Link
Fit a generalized linear model with secure convergence.
glmgam.fit(X, y, coef.start = NULL, tol = 1e-6, maxit = 50, trace = FALSE)
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 |
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.
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 |
Gordon Smyth and Yunshun Chen
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
glmgam.fit is called by mixedModel2Fit.
glm is the standard glm fitting function in the stats package.
y <- rgamma(10, shape=5) X <- cbind(1, 1:10) fit <- glmgam.fit(X, y, trace=TRUE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.