Re: st: fit index for ordered logistic regression

[David Hoaglin <dchoaglin@gmail.com>]

Hi, Julie.

I'm not sure what you mean by "imbedded."

AIC is not an absolute measure.  If you have a set of models that
should be good models on substantive grounds, you can choose the model
that has the smallest AIC.  Those models do not need to be nested, and
they do not have to be based on the same family of distributions (as
long as the calculation of AIC includes all the constants in the
likelihood).  The usual definition of AIC applies to large samples.
If the ratio of the sample size (n) to the total number of parameters
(K) is not "large," say n/K < 40, it is better to use a modified
version of AIC.

Are you able to use deviance (and DIC) to compare each of your models
against the corresponding "saturated" model?

Also, Agresti (2010) discusses ways of assessing goodness of fit.

David Hoaglin

Agresti, A. (2010).  Analysis of Ordinal Categorical Data, 2nd ed.
John Wiley & Sons.


On Sat, Dec 14, 2013 at 2:48 PM, Julie Lamoureux <drjuliel@comcast.net> wrote:
> Good day,
>
> I am looking for "normed" goodness of fit indices for ordered logistic
> regression.  We submitted a manuscript with the results of 2 OLR (on two
> different unrelated outcomes) and the reviewer is asking how we assessed
> model goodness of fit and its usefulness.  I know AIC and BIC let us compare
> models that are "imbedded" but can we use AIC and BIC to determine how
> useful the model is?  If so, what "cut-off" for those indices can be
> considered "good"?  Is there something else I am not aware of to answer this
> question?
>
> Thank you for your time
>
> Julie Lamoureux
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