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
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