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,
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.
