AIC: AIC and BIC for hyper-Poisson Fits

In DGLMExtPois: Double Generalized Linear Models Extending Poisson Regression

AIC and BIC for hyper-Poisson Fits

Description

Computes the Akaike's information criterion or the Schwarz's Bayesian criterion for hyper-Poisson Fits

Usage

## S3 method for class 'glm_hP'
AIC(object, ..., k = 2)

## S3 method for class 'glm_hP'
BIC(object, ...)

Arguments

object	an object of class "glm_hP", typically the result of a call to glm.hP.
...	optionally more fitted model objects.
k	numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Examples

## Fit a hyper-Poisson model
Bids$size.sq <- Bids$size ^ 2
fit <- glm.hP(formula.mu = numbids ~ leglrest + rearest + finrest +
              whtknght + bidprem + insthold + size + size.sq + regulatn,
              formula.gamma = numbids ~ 1, data = Bids)

## Obtain its AIC and BIC
AIC(fit)
BIC(fit)

DGLMExtPois documentation built on Sept. 4, 2023, 5:06 p.m.