Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.

# Re: st: ordered logistic integration problems

 From Richard Williams To statalist@hsphsun2.harvard.edu, "statalist@hsphsun2.harvard.edu" Subject Re: st: ordered logistic integration problems Date Wed, 20 Mar 2013 19:20:54 -0400

```Occasionally adding the -difficult- option will work miracles.

```
My guess, that you are spreading the data too thin. If I follow you, the DV has 12 values, and 90% of the cases are a 1, which means the other 11 values average less than 1% of the cases. With gologit2 you are estimating 11 sets of coefficients. I am not surprised you have to collapse to only 3 categories.
```
```
But why are you using an ordinal model in the first place? Why not a model specifically designed for proportions? See, for example,
```
http://www.stata.com/support/faqs/statistics/logit-transformation/

http://www.ats.ucla.edu/stat/stata/faq/proportion.htm

At 06:04 PM 3/20/2013, Bontempo, Daniel E wrote:
```
Can anyone explain the kind of data conditions that cause gllamm or glogit2 to spit out:
```
flat or discontinuous region encountered
numerical derivatives are approximate
nearby values are missing
could not calculate numerical derivatives
missing values encountered
r(430);

```
I have a colleague with proportion data that only has about 12 discrete values between 0 and 1 with about 90% 1's. Skew -3.27, Kurtosis>15.
```
```
We want to model for 3 groups (between) and 3 occasions (within). Prior work published in 2000, had similar proportions and used HML (Gaussian) and got interpretable results. After looking at the distributions, I suggested ologit might be more appropriate than regress.
```
```
I was already concerned about these proportion DVs because my colleague has calculated proportion correct of however many scorable events there were, and the number of events differs a lot from subject to subject. Some have 2 some have 10. BUT - my question for the moment is technical difficulty with numerical derivatives.
```
```
Since there is occasion nested within person, I was interested in gllamm with the ologit link, as well as robust ologit with "cluster(subject)". I also tried glogit2 because I was unsure the parallel regression assumption was met.
```
```
I easily get ologit to run. However both gllamm and glogit2 make similar complaints about missing or discontinuous numerical derivatives and do not complete. I tried the log-log link in glogit2 since the values rise slowly from 0 and suddenly go to 1. I kept rounding to get fewer levels.
```
```
I have to collapse to only 3 levels to get glogit2 to run. gllamm keeps telling me to use trace and check initial model, but when I do I see reasonable fixed effect values.
```
```
Is ologit able to use an estimation method that avoids these integration issues?
```
```
I am trying to get the disaggregated data so multilevel logistic regressions can be done, but it is not clear disaggregated data will be available.
```
Any pointers, advice, suggestions, references ... all appreciated.

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/
```
```
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/
```