ml_glm3: A reduced maximum likelihood fitting function that omits null...
In msme: Functions and Datasets for "Methods of Statistical Model Estimation"

Description Usage Arguments Details Value Author(s) References See Also Examples

This function fits generalized linear models by maximizing the joint log-likeliood, which is set in a separate function. Null models are omitted from the fit. The post-estimation output is designed to work with existing reporting functions.

1	ml_glm3(formula, data, family, link, offset = 0, start = NULL, verbose = FALSE, ...)

`formula`	an object of class '"formula"' (or one that can be coerced to that class): a symbolic description of the model to be fitted. (See the help for 'glm' for more details).
`data`	a data frame containing the variables in the model.
`family`	a description of the error distribution be used in the model. This must be a character string naming a family.
`link`	a description of the link function be used in the model. This must be a character string naming a link function.
`offset`	this can be used to specify an _a priori_ known component to be included in the linear predictor during fitting. This should be 0 or a numeric vector of length equal to the number of cases.
`start`	optional starting points for the parameter estimation.
`verbose`	logical flag affecting the detail of printing. Defaults to FALSE.
`...`	other arguments to pass to the likelihood function, e.g. group stucture.

This function is essentially the same as ml_glm, but includes the dots argument to allow a richer set of model likelihoods to be fit, and omits computation of the null deviance. The function is presently set up to only fit the conditional fixed-effects negative binomial model.

`fit`	the output of optim.
`X`	the design matrix.
`y`	the response variable.
`call`	the call used for the function.
`obs`	the number of observations.
`df.null`	the degrees of freedom for the null model.
`df.residual`	the residual degrees of freedom.
`deviance`	the residual deviance.
`null.deviance`	the residual deviance for the null model, set to NA.
`residuals`	the deviance residuals.
`coefficients`	parameter estimates.
`se.beta.hat`	standard errors of parameter estimates.
`aic`	Akaike's Information Criterion.
`i`	the number of iterations required for convergence.

Andrew Robinson and Joe Hilbe.

Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.

irls, glm, ml_glm

data(medpar)
med.nb.g <- ml_glm3(los ~ hmo + white,
                   family = "gNegBinomial", 
                   link = "log",
                   group = medpar$provnum, 
                   data = medpar)
summary(med.nb.g)