oneinfl (version 1.0.2)

oneLRT: Likelihood Ratio Test for Nested Models

Description

Performs a likelihood ratio test (LRT) to compare two nested models estimated by oneinfl or truncreg. It calculates the LRT statistic and its associated p-value, testing whether the more complex model provides a significantly better fit to the data than the simpler model.

Usage

oneLRT(mod0, mod1)

Value

A list with the following components:

LRTstat

The likelihood ratio test statistic.

pval

The p-value associated with the test statistic, based on a chi-squared distribution.

Arguments

mod0

A model object (typically the simpler model) estimated using oneinfl or truncreg.

mod1

A model object (typically the more complex model) estimated using oneinfl or truncreg.

Details

The function extracts the log-likelihoods and number of parameters from the two models. It then calculates the LRT statistic: $$-2 \cdot (\ell_0 - \ell_1)$$ where \(\ell_0\) and \(\ell_1\) are the log-likelihoods of the simpler and more complex models, respectively. The degrees of freedom for the test are equal to the difference in the number of parameters between the models.

The likelihood ratio test is commonly used to test for: - Overdispersion: Comparing a Poisson model to a negative binomial model. - One-inflation: Comparing a one-inflated model to a non-one-inflated model.

See Also

oneinfl for fitting one-inflated models. truncreg for fitting zero-truncated models. pchisq for the chi-squared distribution.

Examples

# Example: One-inflation test
df <- data.frame(y = rpois(100, lambda = 5), x = rnorm(100), z = rnorm(100))
OIZTNB <- oneinfl(y ~ x | z, df = df, dist = "negbin")
ZTNB <- truncreg(y ~ x, df = df, dist = "negbin")
oneLRT(OIZTNB, ZTNB)

# Example: Overdispersion test
OIPP <- oneinfl(y ~ x | z, df = df, dist = "Poisson")
oneLRT(OIZTNB, OIPP)