Gamma
generalized linear model.
gamma.shape.glm(object, it.lim = 10, eps.max = sqrt(.Machine$single.eps), verbose = F, ...)
Gamma
family
or
quasi
family with
variance = mu^2
.
T
, causes successive iterations to be
printed out. The initial estimate is taken from the deviance.
A glm fit for a Gamma family correctly calculates the maximum likelihood estimate of the mean parameters but provides only a crude estimate of the dispersion parameter. This function takes the results of the glm fit and solves the maximum likelihood equation for the reciprocal of the dispersion parameter, which is usually called the shape (or exponent) parameter.
clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,42,35,27,25,21,19,18), lot2 = c(69,35,26,21,18,16,13,12,12)) clot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma) gamma.shape(clot1) gm <- glm(Days + 0.1 ~ Age*Eth*Sex*Lrn, quasi(link = log, variance = mu^2), quine) gamma.shape(gm, verbose = T) summary(gm, dispersion = gamma.dispersion(gm)) # better summary