Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes the Rsquared measures for binomial GLMs proposed by Tjur (2010) "Coefficients of determination in logistic regression models  a new proposal: The coefficient of discrimination".
1 2 3 4 5 6 7 8 
object 
a binomial 
x 
an 
which 
the desired plot: histograms, empirical cumulative distribution functions or ROC (receiver operating characteristic) curve 
digits 
the desired number of printed digits 
... 
currently not used 
The plot method has the following options
"hist"
Two histograms with ten bins of the fitted probabilities are plottet
on top of each other; the upper one for y = 0
and the lower
one for y = 1
.
"ecdf"
Two ecdf curves; one for y = 0
and one for y = 1
"ROC"
The (empirical) ROC curve
Rsq.glm
returns an object of class Rsq
. The plot
and print
methods returns the Rsq
objects invisibly.
Rune Haubo B Christensen
Tjur, T. (2009) Coefficients of determination in logistic regression models  a new proposal: The coefficient of discrimination. The American Statistician, 63(4), 366372.
A HLtest
(Hosmer and Lemeshow test)
method exists for Rsq
objects.
1 2 3 4 5 6 7 8 9 10 11 12 13  ## Lifted from example(predict.glm):
ldose < rep(0:5, 2)
numdead < c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex < factor(rep(c("M", "F"), c(6, 6)))
SF < cbind(numdead, numalive=20numdead)
budworm.lg < glm(SF ~ sex*ldose, family=binomial)
## summary(budworm.lg)
(Rsq.budworm < Rsq(budworm.lg))
plot(Rsq.budworm, "hist") ## or simply 'plot(Rsq.budworm)'
plot(Rsq.budworm, "ecdf")
plot(Rsq.budworm, "ROC")

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