Technical Report, 20-01, Department of Statistics, Purdue University. Coefficients of determination for generalized linear mixed models. A coefficient of determination for generalized linear models. (1991) A note on a general definition of the coefficient of determination. (1990) R^2 measures based on Wald and likelihood ratio joint significance tests. (1983) Limited-Dependent and Qualitative Variables in Econometrics. Journal of the American Statistical Association, 73: 113-121. (1978) Regression and ANOVA with zero-one data: measures of residual variation. (1989) The Analysis of Binary Data, 2nd ed. The Nagelkerkes R squared means the power of explanation of the model. (1997) An R-squared measure of goodness of fit for some common nonlinear regression models. Description To evaluate the goodness of fit of the logistic regression model, calculating Nagelkerkes R squared from the result of glm (). For (generalized) linear mixed models, there are three types of R^2 calculated on the basis of observed response values, estimates of fixed effects, and variance components, i.e., model-based R_M^2 (proportion of variation explained by the model in total, including both fixed-effects and random-efffects factors), fixed-effects R_F^2 (proportion of variation explained by the fixed-effects factors), and random-effects R_R^2 (proportion of variation explained by the random-effects factors). DetailsĬalculate the R-squared for (generalized) linear models. Proportion of variation explained by the random-effects factors. Proportion of variation explained by the fixed-effects factors.
Proportion of variation explained by the model in total, including both fixed-effects and random-efffects factors. For (generalized) linear mixed models, R_M^2 'n' - corrected version of 'lr' (Nagelkerke, 1991), calling rsq.n. 'lr' - likelihood-ratio-based (Maddala, 1983 Cox and Snell, 1989 Magee, 1990), calling rsq.lr 'sse' - SSE-based (Efron, 1978), calling rsq.sse 'kl' - KL-divergence-based (Cameron and Windmeijer, 1997), calling rsq.kl 'v' (default) - variance-function-based (Zhang, 2016), calling rsq.v The type of R-squared (only applicable for generalized linear models):
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