gamma.shape
From MASS v7.347
by Brian Ripley
Find the maximum likelihood estimate of the shape parameter of
the gamma distribution after fitting a Gamma
generalized
linear model.
 Keywords
 models
Usage
# S3 method for glm
gamma.shape(object, it.lim = 10,
eps.max = .Machine$double.eps^0.25, verbose = FALSE, …)
Arguments
 object

Fitted model object from a
Gamma
family orquasi
family withvariance = "mu^2"
.  it.lim
 Upper limit on the number of iterations.
 eps.max
 Maximum discrepancy between approximations for the iteration process to continue.
 verbose

If
TRUE
, causes successive iterations to be printed out. The initial estimate is taken from the deviance.  …
 further arguments passed to or from other methods.
Details
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.
Value
List of two components
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
See Also
Examples
library(MASS)
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,
start = c(3, rep(0,31)))
gamma.shape(gm, verbose = TRUE)
summary(gm, dispersion = gamma.dispersion(gm)) # better summary
Community examples
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