Measures of model fit statistics for multiple models.
Usage
blr_multi_model_fit_stats(model, ...)
# Default S3 method
blr_multi_model_fit_stats(model, ...)See also
Other model fit statistics:
blr_model_fit_stats(),
blr_pairs(),
blr_rsq_adj_count(),
blr_rsq_cox_snell(),
blr_rsq_effron(),
blr_rsq_mcfadden_adj(),
blr_rsq_mckelvey_zavoina(),
blr_rsq_nagelkerke(),
blr_test_lr()
Examples
model <- glm(honcomp ~ female + read + science, data = hsb2,
family = binomial(link = 'logit'))
model2 <- glm(honcomp ~ female + read + math, data = hsb2,
family = binomial(link = 'logit'))
blr_multi_model_fit_stats(model, model2)
#> Measures Model 1 Model 2
#> loglik_null Log-Lik Intercept Only -115.644 -115.644
#> loglik_model Log-Lik Full Model -80.118 -75.210
#> m_deviance Deviance 160.236 150.420
#> lr_ratio LR 71.052 80.869
#> lr_pval Prob > LR 0.000 0.000
#> mcfadden MCFadden's R2 0.307 0.350
#> adj_mcfadden McFadden's Adj R2 0.273 0.315
#> m_aic ML (Cox-Snell) R2 168.236 158.420
#> cox_snell Cragg-Uhler(Nagelkerke) R2 0.299 0.333
#> m_bic McKelvey & Zavoina's R2 181.430 171.613
#> mckelvey Efron's R2 0.518 0.523
#> effron Count R2 0.330 0.379
#> nagelkerke Adj Count R2 0.436 0.485
#> count_r2 AIC 0.810 0.830
#> count_adj BIC 0.283 0.358