Note that the Nagelkerke and Estrella coefficients are designed for discrete dependent variables and thus cannot be used in this context. Instead, the Cox and Snell coefficient is recommended, along with the pseudo-\(R^2\). It is worth noting that McFadden's index may produce negative values and should be avoided.
da.betareg.fit(original.model, newdata = NULL, ...)A function described by using-fit-indices. You could retrieve following indices:
r2.pseudoProvided by betareg by default
r2.mMcFadden(1974)
r2.csCox and Snell(1989).
Original fitted model
Data used in update statement
ignored
Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London, UK: Chapman and Hall.
Estrella, A. (1998). A new measure of fit for equations with dichotomous dependent variables. Journal of Business & Economic Statistics, 16(2), 198-205. doi: 10.1080/07350015.1998.10524753.
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 104-142). New York, NY: Academic Press.
Shou, Y., & Smithson, M. (2015). Evaluating Predictors of Dispersion:A Comparison of Dominance Analysis and Bayesian Model Averaging. Psychometrika, 80(1), 236-256.
Other fit indices:
da.clm.fit(),
da.dynlm.fit(),
da.glm.fit(),
da.lm.fit(),
da.lmWithCov.fit(),
da.lmerMod.fit(),
da.mlmWithCov.fit()