Likelihood Ratio Test for nested COM-Poisson models

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.

Value

An object of class "htest" (see t.test for the generic structure), with components:

statistic: the likelihood ratio test statistic.
parameter: the degrees of freedom for the test statistic.
p.value: the p-value for the test.
method: a character string describing the test.
data.name: a character string giving the names of the two model objects compared.

Printing the returned object (e.g. via automatic printing at the console) displays a formatted summary of the test.

References

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

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
#> 
#> 	Likelihood ratio test for testing both COM-Poisson models are
#> 	equivalent
#> 
#> data:  M.bids.full vs. M.bids.null
#> LR statistic = 13.7, df = 1, p-value = 0.0002139
#> 

## 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)
#> 
#> 	Likelihood ratio test for testing both COM-Poisson models are
#> 	equivalent
#> 
#> data:  M.sit.null1 vs. M.sit.full
#> LR statistic = 0.439, df = 3, p-value = 0.9321
#> 

## Fit null model; using constant dispersion specification
M.sit.null2 <- update(M.sit.full, formula_nu. = NULL)
cmplrtest(M.sit.null2, M.sit.full)
#> 
#> 	Likelihood ratio test for testing both COM-Poisson models are
#> 	equivalent
#> 
#> data:  M.sit.null2 vs. M.sit.full
#> LR statistic = 0.439, df = 3, p-value = 0.9321
#>