Description Usage Arguments References See Also Examples
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.
1 | cmplrtest(object1, object2, digits = 3)
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object1 |
an object class 'cmp', obtained from a call to |
object2 |
an object class 'cmp', obtained from a call to |
digits |
numeric; minimum number of significant digits to be used for most numbers. |
Huang, A. (2017). Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts. Statistical Modelling 17, 359–380.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## 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)
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