glm.hermite: Maximum likelihood estimation and Hermite regression

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

View source: R/glm.hermite.R


glm.hermite is used to fit generalized linear models with count responses following a Hermite distribution, specified by giving a symbolic description of the linear predictor. A summary method providing the most meaningful information on the fitted model is available for objects of class glm.hermite.


  glm.hermite(formula, data, link="log", start=NULL, m = NULL)



symbolic description of the model. A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response.


an optional data frame containing the variables in the model.


character specification of link function: "log" or "identity". By default link="log".


a vector containing the starting values for the parameters of the specified model. Its default value is NULL.


value for parameter m. Its default value is NULL, and in that case it will be estimated inside the function.


glm.hermite returns an object of class glm.hermite, which is a list including the following components:


María Oliveira, Manuel Higueras, David Moriña and Pere Puig


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Puig P. (2003). Characterizing Additively Closed Discrete Models by a Property of Their Maximum Likelihood Estimators, with an Application to Generalized Hermite Distributions. Journal of the American Statistical Association 2003; 98:687–692.

See Also

Distributions for some other distributions, qhermite, phermite, rhermite, hermite-package


data <- c(rep(0,122), rep(1,40), rep(2,14), rep(3,16), rep(4,6), rep(5,2))
mle1 <- glm.hermite(data~1, link="log", start=NULL, m=3)

hermite documentation built on May 30, 2017, 3:07 a.m.