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
Dunno. You might wish to email the maintainer (see ?maintainer), who may not monitor this list, if you do not get a satisfactory reply here. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Jul 26, 2017 at 7:14 AM, john polo <jpolo at mail.usf.edu> 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 at 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.
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 > > >
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