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
______________________________________________
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.
