Performs a likelihood ratio test comparing two nested hurdle demand models. Typically used to test whether adding the random effect on alpha (c_i) significantly improves model fit (3-RE vs 2-RE models).
Value
Invisibly returns a list with:
- lr_stat
Likelihood ratio test statistic
- df
Degrees of freedom
- p_value
P-value from chi-squared distribution
- model_comparison
Data frame with model comparison statistics
Examples
# \donttest{
data(apt)
fit3 <- fit_demand_hurdle(apt, y_var = "y", x_var = "x", id_var = "id",
random_effects = c("zeros", "q0", "alpha"))
#> Sample size may be too small for reliable estimation.
#> Subjects: 10, Parameters: 12, Recommended minimum: 60 subjects.
#> Consider using more subjects or the simpler 2-RE model.
#> Fitting HurdleDemand3RE model...
#> Part II: zhao_exponential
#> Subjects: 10, Observations: 160
#> Fixed parameters: 12, Random effects per subject: 3
#> Optimizing...
#> Converged in 81 iterations
#> Computing standard errors...
#> Done. Log-likelihood: 32.81
fit2 <- fit_demand_hurdle(apt, y_var = "y", x_var = "x", id_var = "id",
random_effects = c("zeros", "q0"))
#> Sample size may be too small for reliable estimation.
#> Subjects: 10, Parameters: 9, Recommended minimum: 45 subjects.
#> Consider using more subjects or the simpler 2-RE model.
#> Fitting HurdleDemand2RE model...
#> Part II: zhao_exponential
#> Subjects: 10, Observations: 160
#> Fixed parameters: 9, Random effects per subject: 2
#> Optimizing...
#> Converged in 95 iterations
#> Computing standard errors...
#> Done. Log-likelihood: 2.31
compare_hurdle_models(fit3, fit2)
#>
#> Likelihood Ratio Test
#> =====================
#> Model n_RE LogLik df AIC BIC
#> Full (3 RE) 3 32.81453 12 -41.62905 -4.726965
#> Reduced (2 RE) 2 2.30934 9 13.38132 41.057884
#>
#> LR statistic: 61.0104
#> df: 3
#> p-value: 3.5757e-13
# }