glm.nb { MASS } | R Documentation |
A modification of the system function < code >glm() to include estimation of the additional parameter, < code >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 < code >glm() function. Note that these exclude < code >family and < code >offset (but < code >offset() can be used). |
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 < code >log, < code >sqrt or < code >identity. |
An alternating iteration process is used. For given < code >theta the GLM is fitted using the same process as used by < code >glm(). For fixed means the < code >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 < code >theta are controlled by the < code >maxit parameter of < code >glm.control.)
Setting < code >trace > 0 traces the alternating iteration process. Setting < code >trace > 1 traces the < code >glm fit, and setting < code >trace > 2 traces the estimation of < code >theta.
A fitted model object of class < code >negbin inheriting from < code >glm and < code >lm. The object is like the output of < code >glm but contains three additional components, namely < code >theta for the ML estimate of theta, < code >SE.theta for its approximate standard error (using observed rather than expected information), and < code >twologlik for twice the log-likelihood function.
Venables, W. N. and Ripley, B. D. (2002) < em >Modern Applied Statistics with S. Fourth edition. Springer.
< code >glm, < code >negative.binomial, < code >anova.negbin, < code >summary.negbin, < code >theta.md
There is a < code >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)