[R] How long to wait for process?

[Marc Schwartz]

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 <jpolo at 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 
>>> 
>>> 
>>