AIC_CMP: AIC and BIC for COM-Poisson Fitted Models

In DGLMExtPois: Double Generalized Linear Models Extending Poisson Regression

AIC and BIC for COM-Poisson Fitted Models

Description

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

Usage

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

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

Arguments

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

Examples

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

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

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