Marc,
Sorry for the lack of info on my part. Yes, I did use 'family =
binomial' and I did drop the 3rd level before running the model. I think
the str(<subset>) that I wrote into my original email might not have
been my final step before using glm. Thank you for reminding of the
potential problem.
I think Michael Friendly's idea is probably the solution I need to
consider. I am simplifying my factors a little bit and revising which I
will keep.
best,
John
On 7/27/2017 8:54 AM, Marc Schwartz wrote:
Hi,
Late to the thread here, but I noted that your dependent variable 'know_fin'
has 3 levels in the str() output below.
Since you did not provide a full c&p of your glm() call, we can only presume
that you did specify 'family = binomial' in the call.
Is the dataset 'knowf3' the result of a subsetting operation, such that there
are only two of the three levels of 'know_fin' retained in the records used in
the glm() call, or are there actually 3 levels in the dataset used in the glm()
call?
If the latter, that will of course be problematic and from a quick check here,
glm(..., family = binomial) does not issue a warning or error in the case where
the dependent variable has >2 levels.
Regards,
Marc Schwartz
On Jul 27, 2017, at 8:26 AM, john polo<jp...@mail.usf.edu> wrote:
Michael,
Thank you for the suggestion. I will take your advice and look more critically
at the covariates.
John
On 7/27/2017 8:08 AM, Michael Friendly wrote:
Rather than go to a penalized GLM, you might be better off investigating the
sources of quasi-perfect separation and simplifying the model to avoid or
reduce it. In your data set you have several factors with large number of
levels, making the data sparse for all their combinations.
Like multicolinearity, near perfect separation is a data problem, and is often
better solved by careful thought about the model, rather than wrapping the data
in a computationally intensive band aid.
-Michael
On 7/26/2017 10:14 AM, john polo wrote:
UseRs,
I have a dataframe with 2547 rows and several hundred columns in R 3.1.3. I am
trying to run a small logistic regression with a subset of the data.
know_fin ~
comp_grp2+age+gender+education+employment+income+ideol+home_lot+home+county
> str(knowf3)
'data.frame': 2033 obs. of 18 variables:
$ userid : Factor w/ 2542 levels "FNCNM1639","FNCNM1642",..: 1857 157
965 1967 164 315 849 1017 699 189 ...
$ round_id : Factor w/ 1 level "Round 11": 1 1 1 1 1 1 1 1 1 1 ...
$ age : int 67 66 44 27 32 67 36 76 70 66 ...
$ county: Factor w/ 80 levels "Adair","Alfalfa",..: 75 75 75 75 75 75 64
64 64 64 ...
$ gender : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 2 1 2 2 ...
$ education : Factor w/ 8 levels "1","2","3","4",..: 6 7 6 8 2 4 2 4 2 6
...
$ employment: Factor w/ 9 levels "1","2","3","4",..: 8 4 4 4 3 8 5 8 4 4
...
$ income : num 550000 80000 90000 19000 42000 30000 18000 50000 800000
10000 ...
$ home: num 0 0 0 0 0 0 0 0 0 0 ...
$ ideol : Factor w/ 7 levels "1","2","3","4",..: 2 7 4 3 2 4 2 3 2 6
...
$ home_lot : Factor w/ 3 levels "1","2","3": 2 2 2 2 2 2 3 3 1 2 ...
$ hispanic : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ comp_grp2 : Factor w/ 16 levels "Cr_Gr","Cr_Ot",..: 13 13 13 13 13 13 10
10 10 10 ...
$ know_fin : Factor w/ 3 levels "0","1","2": 2 2 2 2 2 2 2 2 2 2 ...
With the regular glm() function, I get a warning about "perfect or quasi-perfect
separation"[1]. I looked for a method to deal with this and a penalized GLM is an
accepted method[2]. This is implemented in logistf(). I used the default settings for the
function.
Just before I run the model, memory.size() for my session is ~4500 (MB).
memory.limit() is ~25500. When I start the model, R immediately becomes
non-responsive. This is in a Windows environment and in Task Manager, the
instance of R is, and has been, using ~13% of CPU aand ~4997 MB of RAM. It's
been ~24 hours now in that state and I don't have any idea of how long this
should take. If I run the same model in the same setting with the base glm(),
the model runs in about 60 seconds. Is there a way to know if the process is
going to produce something useful after all this time or if it's hanging on
some kind of problem?
[1]:https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression#68917
[2]:https://academic.oup.com/biomet/article-abstract/80/1/27/228364/Bias-reduction-of-maximum-likelihood-estimates
--
Men occasionally stumble
over the truth, but most of them
pick themselves up and hurry off
as if nothing had happened.
-- Winston Churchill
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
