cmplrtest: Likelihood Ratio Test for nested COM-Poisson models

In thomas-fung/mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Likelihood Ratio Test for nested COM-Poisson models

Description

Perform a likelihood ratio chi-squared test between nested COM-Poisson models. The test statistics is calculated as 2*(llik- llik_0). The test statistics has degrees of freedom r where r is the difference in the number of parameters between the full and null models.

Usage

cmplrtest(object1, object2, digits = 3)

Arguments

object1	an object class 'cmp', obtained from a call to glm.cmp
object2	an object class 'cmp', obtained from a call to glm.cmp
digits	numeric; minimum number of significant digits to be used for most numbers.

References

Huang, A. (2017). Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts. Statistical Modelling 17, 359–380.

See Also

glm.cmp, update.cmp

Examples

## Testing for the mean coefficients
data(takeoverbids)

## Fit full model
M.bids.full <- glm.cmp(numbids ~ leglrest + rearest + finrest + whtknght
  + bidprem + insthold + size + sizesq + regulatn, data = takeoverbids)

## Fit null model; without whtknght
M.bids.null <- update(M.bids.full, . ~ . - whtknght)

## Likelihood ratio test for the nested models
cmplrtest(M.bids.full, M.bids.null) # order of objects is not important

## Testing for dispersion coefficients
data(sitophilus)
M.sit.full <- glm.cmp(formula = ninsect ~ extract, formula_nu = ~extract, data = sitophilus)

## Fit null model; dropping extract from dispersion equation
M.sit.null1 <- update(M.sit.full, formula_nu. = ~1)
cmplrtest(M.sit.null1, M.sit.full)

## Fit null model; using constant dispersion specification
M.sit.null2 <- update(M.sit.full, formula_nu. = NULL)
cmplrtest(M.sit.null2, M.sit.full)

thomas-fung/mpcmp documentation built on June 13, 2022, 6:20 p.m.