Re: [R] R-squared in Logistic Regression

From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>
Date: Tue 29 Mar 2005 - 22:40:32 EST

Johan Stenberg wrote:
> Dear all,
>
> How do I make R show the R-squared (deviance explained by the model) in
> a logistic regression?
>
> Below is how I write my syntax. Basically I want to investigate
> density-dependence in parasitism of larvae. Note that in the end I
> perform a F-test because the dispersion factor (residual deviance /
> residual df) is significantly higher than 1. But how do I make R show
> the "R-squared"?
>
> Best wishes
> Johan

The proportion of deviance explained has been shown to not be such a good measure. You can use the lrm function in the Design package to get various measures including ROC area (C index), Somers' Dxy and Kendall tau rank correlation, Nagelkerke generalization of R-squared for maximum likelihood-based models (related to Maddala and others).

Frank Harrell

>
>

>>y<-cbind(para,unpara)
>>model<-glm(y~log(larvae),binomial)
>>summary(model)

>
>
> Call:
> glm(formula = y ~ log(larvae), family = binomial)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -2.0633 -1.6218 -0.1871 0.7907 2.7670
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 1.0025 0.7049 1.422 0.15499
> log(larvae) -1.0640 0.3870 -2.749 0.00597 **
>
> (Dispersion parameter for binomial family taken to be 1)

>
> Null deviance: 35.981 on 12 degrees of freedom
> Residual deviance: 27.298 on 11 degrees of freedom
> AIC: 40.949
>
> Number of Fisher Scoring iterations: 4
>
>
>>anova(model,test="F")

>
> Analysis of Deviance Table
>
> Model: binomial, link: logit
>
> Response: y
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev F Pr(>F)
> NULL 12 35.981
> log(larvae) 1 8.683 11 27.298 8.6828 0.003212 **
>
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-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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Received on Tue Mar 29 22:48:37 2005

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