modEvA (version 3.5)

RsqGLM: R-squared measures for GLMs

Description

This function calculates some (pseudo) R-squared statistics for binomial Generalized Linear Models.

Usage

RsqGLM(model = NULL, obs = NULL, pred = NULL, use = "pairwise.complete.obs",
plot = TRUE, ...)

Value

The function returns a named list of the calculated R-squared values.

Arguments

model

a binary-response model object of class "glm". Alternatively, you can input the 'obs' and 'pred' arguments instead of 'model'.

obs

alternatively to 'model' and together with 'pred', a vector of observed presences (1) and absences (0) of a binary response variable. This argument is ignored if 'model' is provided.

pred

alternatively to 'model' and together with 'obs', a vector with the corresponding predicted values of presence probability. Must be of the same length and in the same order as 'obs'. This argument is ignored if 'model' is provided.

use

argument to be passed to cor for handling mising values.

plot

logical value indicating whether or not to display a barplot of the calculated measures.

...

additional arguments to pass to the plot function (see Examples).

Author

A. Marcia Barbosa

Details

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.

References

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.

See Also

Dsquared, AUC, threshMeasures, HLfit

Examples

# 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)


# plotting arguments can be modified:

par(mar = c(6, 3, 2, 1))

RsqGLM(obs = mod$y, pred = mod$fitted.values, col = "seagreen", border = NA, 
ylim = c(0, 1), las = 2, main = "Pseudo-R-squared values")