RsqGLM: R-squared measures for GLMs

In modEvA: Model Evaluation and Analysis

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, plot.type = "lollipop", ...)

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 bar chart or (by default) a lollipop chart of the calculated measures.
plot.type	character value indicating the type of plot to produce (if plot=TRUE). Can be "lollipop" (the default) or "barplot".
...	additional arguments to pass to the plot function (see Examples).

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.

Value

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

Note

Tjur's R-squared can only be calculated for models with binomial response variable; otherwise, NA will be returned.

Author(s)

A. Marcia Barbosa

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), main = "Pseudo-R-squared values")

modEvA documentation built on Oct. 30, 2024, 1:06 a.m.