RSiteSearch("R-squared for glm") produced 17 hits, the second of
which cited three articles that might interest you
(http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48688.html). Other
comments suggest that the percent of deviance explained may not be
terribly useful (e.g.,
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48689.html).
If this is not adequte, PLEASE do read the posting guide!
"http://www.R-project.org/posting-guide.html"; and submit another
question. If you follow the posting guide, I believe it will likely
shorten the time it takes you to solve whatever problem brought you to R.
spencer graves
alexbri wrote:
> Hi:
>
>
>
> I'm working with multinomial models with library nnet, and I'm trying to get
> the explained deviance (pseudo R^2) of my models.
>
> I am assuming that:
>
> pseudo R^2= 1 - dev(model) / dev (null)
>
> where dev(model) is the deviance for the fitted model and dev(null) is the
> deviance for the null model (with the intercept only).
>
>
>
> library(nnet)
>
> full.model<- multinom(cbind(factor1, factor2 ,., factor5) ~ x1 + x2 + x3,
> weights=total, data=mydata)
>
> null.model<- multinom(cbind(factor1, factor2 ,., factor5) ~ +1,
> weights=total, data=mydata)
>
>
>
> Then I calculated
>
> pseudoR^2 = 1 - full.model$deviance / null.model$deviance
>
>
>
> I'm obtaining very low values for pseudoR^2 (there is not much difference
> between the deviances of the two models). full.model fits (graphically) very
> well to the data , so I think that the problem is in the null.model (maybe it
> is not well defined) or with the calculus of pseudoR^2.
>
>
>
> Can someone please give me some suggestions about this?
>
> Thanks in advance
>
> alex
>
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