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