[R] proportional odds model

Frank E Harrell Jr f.harrell at vanderbilt.edu
Thu Aug 2 14:09:09 CEST 2007

Ramon Martínez Coscollà wrote:
> Hi all!!
> I am using a proportinal odds model to study some ordered categorical
> data. I am trying to predict one ordered categorical variable taking
> into account only another categorical variable.
> I am using polr from the R MASS library. It seems to work ok, but I'm
> still getting familiar and I don't know how to assess goodness of fit.
> I have this output, when using response ~ independent variable:
> Residual Deviance: 327.0956
> AIC: 333.0956
>> polr.out$df.residual
> [1] 278
>> polr.out$edf
> [1] 3
> When taking out every variable... (i.e., making formula: response ~ 1), I have:
> Residual Deviance: 368.2387
> AIC: 372.2387
> How can I test if the model fits well? How can I check that the
> independent variable effectively explains the model? Is there any
> test?
> Moreover, sendig summary(polr.out) I get this error:
> Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) :
>         initial value in 'vmmin' is not finite
> Something to do with the optimitation procedure... but, how can I fix
> it? Any help would be greatly appreciated.
> Thanks.

You might also look at lrm and residuals.lrm in the Design package, 
which provides partial and score residual plots to check PO model 

Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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