R-squared measures for GLMs
This function calculates some (pseudo) R-squared statistics for binomial Generalized Linear Models.
RsqGLM(model = NULL, obs = NULL, pred = NULL, use = "pairwise.complete.obs")
model |
a model object of class "glm". |
obs |
a vector of observed presences (1) and absences (0) or another
binary response variable. Not necessary (and ignored) if |
pred |
a vector with the corresponding predicted values of presence probability. Must be of the same length and in the same order as |
use |
argument to be passed to |
Implemented measures include the R-squareds of McFadden (1974), Cox-Snell (1989), Nagelkerke (1991, which corresponds to the corrected Cox-Snell, eliminating its upper bound), and Tjur (2009). See Allison (2014) for a brief review of these measures.
The function returns a named list of the calculated R-squared values.
Tjur's R-squared can only be calculated for models with binomial response variable; otherwise, NA will be returned.
A. Marcia Barbosa
Allison P. (2014) Measures of fit for logistic regression. SAS Global Forum, Paper 1485-2014
Cox, D.R. & Snell E.J. (1989) The Analysis of Binary Data, 2nd ed. Chapman and Hall, London
McFadden, D. (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P. (ed.) Frontiers in Economics. Academic Press, New York
Nagelkerke, N.J.D. (1991) A note on a general definition of the coefficient of determination. Biometrika, 78: 691-692
Tjur T. (2009) Coefficients of determination in logistic regression models - a new proposal: the coefficient of discrimination. The American Statistician, 63: 366-372.
# load sample models: data(rotif.mods) # choose a particular model to play with: mod <- rotif.mods$models[[1]] RsqGLM(model = mod) # you can also use RsqGLM with vectors of observed and predicted values # instead of a model object: RsqGLM(obs = mod$y, pred = mod$fitted.values)
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