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Re: st: Ocratio gives neither AIC nor BIC

 From David Hoaglin To statalist@hsphsun2.harvard.edu Subject Re: st: Ocratio gives neither AIC nor BIC Date Sat, 28 Dec 2013 21:14:43 -0500

```Dear Marcos,

I have not seen any replies so far to your posting.  Perhaps I can
make a start, though I have more questions than answers.

I did not see any information on your dependent variable, other than
that it is continuous and very positively skewed.  It would help me
(and it may help others) to know more about the nature of that
variable.

Skewness of the dependent variable, considered alone, does not
necessarily prevent you from using it in a regression model.  It is
more important to examine (e.g., in scatterplots) the relation between
the dependent variable and each of the predictor variables.  Some of
those relations may account for the apparent skewness.  After fitting
an initial model, you should examine the residuals.  The various plots
may suggest that you transform the dependent variable (e.g., to a
logarithmic scale).

A GLM is a common alternative to using a transformation, but I don't
understand why you chose the logit link.  With a continuous dependent
variable, I would have expected a log link.

I will stop here.  The rest of your analysis goes in a direction whose
logic I do not understand.

David Hoaglin

On Fri, Dec 27, 2013 at 5:02 PM, Marcos Almeida <virtual.596@gmail.com> wrote:
> Hello, Statalisters,
>
> I have a dataset  whose continuous dependent variable is  very
> positively skewed. I decided to eschew regression analysis, even after
> log-transforming it, because I gather a generalized linear model gives
> better adjustments for this "situation".
>
> After testing with glm family (gaussian) link (logit), it still
> presented signs of needing a better-fit model. Then, I took the
> decision to create a new variable, that is, I transformed the
> dependent variable in quartiles. After that, I got 4 categories(up to
> the 25th percentile; from the 25th to the median; from the median to
> the 75th percentile; from the 75th up to the highest value).
>
> And now comes my question.
>
> I compared several models: the multinomial logit (mlogit) the ordinal
> logit(ologit), the generalized ordered model (gologit2 user-written
> command) and finally, the gologit2 with proportional-odds(autofit
> option)pleased me most. I mean it because the multinomial logit didn't
> comply with the IIA assumption (the much debated Hausman test), the
> ologit didn't comply with the proportional-odds assumption and the
> gologit2 with the autofit option dutifully adjusted for the partial
> proportional-odds.
>
> After doing each modelling, I calculated the AIC and BIC without any
> trouble. However, just for a last try, I decided to perform a
> continuation-ratio model. At first, I found it a reasonable option,
> theoretically speaking.
>
> After installing the user-written ocratio, I did the estimations and
> all seemed to be just fine. But I noticed something wrong: the report
> didn't show the AIC statistic. That came as I surprise.I really don't
> understand what might have happened. I did (almost) everything, I
> mean, in terms of commands I knew:estat ic, for example. Also, I
> installed use-written commands, like fitstat, unfortunately of no
> avail. By the way, I carefully read a book (Generalized Linear Models
> and Extensions, Hardin and Hilbe, StataPress, page 343), and,lo and
> behold, there ocratio gave the AIC after typing "aic". With much hope,
> I typed this command, again of no avail. Sadly
> enough, all I got was the message in red: estimates not found.
>
> I checked the FAQs on the matter as well as potential queries on the
> Web, but nothing was found related to this. And I'm still perplexed.
>
> My software is a weekly updated Stata13 IC. I wonder if you could give
>
> Finally, I heartly thank you for your consideration.
>
> Best regards,
>
> Marcos Almeida,
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