Fit a Negative Binomial Generalized Linear Model
A modification of the system function glm() to include
estimation of the additional parameter, theta, for a
Negative Binomial generalized linear model.
glm.nb(formula, data, weights, subset, na.action,
start = NULL, etastart, mustart,
control = glm.control(...), method = "glm.fit",
model = TRUE, x = FALSE, y = TRUE, contrasts = NULL, ...,
init.theta, link = log)formula, data, weights, subset, na.action, start, etastart,
mustart, control, method, model, x, y, contrasts, ... |
arguments for the |
init.theta |
Optional initial value for the theta parameter. If omitted a moment estimator after an initial fit using a Poisson GLM is used. |
link |
The link function. Currently must be one of |
An alternating iteration process is used. For given theta the GLM
is fitted using the same process as used by glm(). For fixed means
the theta parameter is estimated using score and information
iterations. The two are alternated until convergence of both. (The
number of alternations and the number of iterations when estimating
theta are controlled by the maxit parameter of
glm.control.)
Setting trace > 0 traces the alternating iteration
process. Setting trace > 1 traces the glm fit, and
setting trace > 2 traces the estimation of theta.
A fitted model object of class negbin inheriting from glm
and lm. The object is like the output of glm but contains
three additional components, namely theta for the ML estimate of
theta, SE.theta for its approximate standard error (using
observed rather than expected information), and twologlik for
twice the log-likelihood function.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
There is a simulate method.
quine.nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) quine.nb2 <- update(quine.nb1, . ~ . + Sex:Age:Lrn) quine.nb3 <- update(quine.nb2, Days ~ .^4) anova(quine.nb1, quine.nb2, quine.nb3)
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