Model fit statistics.

blr_model_fit_stats(model, ...)

Arguments

model

An object of class glm.

...

Other inputs.

References

Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. The American Statistician, 54(1), 17-24.

Windmeijer, F. A. G. (1995). Goodness-of-fit measures in binary choice models. Econometric Reviews, 14, 101-116.

Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). New York: John Wiley & Sons.

J. Scott Long & Jeremy Freese, 2000. "FITSTAT: Stata module to compute fit statistics for single equation regression models," Statistical Software Components S407201, Boston College Department of Economics, revised 22 Feb 2001.

Freese, Jeremy and J. Scott Long. Regression Models for Categorical Dependent Variables Using Stata. College Station: Stata Press, 2006.

Long, J. Scott. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage Publications, 1997.

See also

Other model fit statistics: blr_multi_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')) blr_model_fit_stats(model)
#> Model Fit Statistics #> --------------------------------------------------------------------------------- #> Log-Lik Intercept Only: -115.644 Log-Lik Full Model: -80.118 #> Deviance(196): 160.236 LR(3): 71.052 #> Prob > LR: 0.000 #> MCFadden's R2 0.307 McFadden's Adj R2: 0.273 #> ML (Cox-Snell) R2: 0.299 Cragg-Uhler(Nagelkerke) R2: 0.436 #> McKelvey & Zavoina's R2: 0.518 Efron's R2: 0.330 #> Count R2: 0.810 Adj Count R2: 0.283 #> BIC: 181.430 AIC: 168.236 #> --------------------------------------------------------------------------------- #>