Dear all,
I'm coming with a question concerning logistic regression and SPSS. We're in front of a situation here where we have very few "respondents" (1 in the field to predict) in a logistic regression. Only 1700 on 60000. I think it's a situation called "sparsity", isn't it ? When doing a logistic regression, we have a low fit. As I see it, it's because of this sparse dataset. I was told that a way to solve that kind of problem in LR, is to weight the responding cases, to "artificially" raise their representativity in the dataset. I've looked that up in the classical "bibles" of logistic regression (Menard, Lemeshow, Jaccard), but haven't found any discussion of sparsity, or situations with few respondents. In SPPS, I know there is a weight feature. Does it work with logistic regression ? Is it really a "technique" to (artificially) have a better fit ? What modelling techniques are better suited for sparse datasets, in your opinion ? Thank you so much for helping out ! Marc. |
Hi Marc,
>In SPPS, I know there is a weight feature. Does it work with logistic regression ? Yes, it works well together. >Is it really a "technique" to (artificially) have a better fit ? Yes, you get a "better" fit, but in a sense it is rather self-deception. In reality, the fit is still bad and you cannot rely on the results. >What modelling techniques are better suited for sparse datasets, in your opinion ? SPSS has its exact tests, they are devised for sparse data. Of course, they cannot create significant results where there is nothing significant. Moreover, I do not understand your phrase "1700 on 60000" (sorry for my bad English). If it means that you have 60,000 respondents and that 1700 of them has 1 in the dependent variable and the rest has 0 here, then the case is not about sparsity. 1700 is enough for most practical purposes and you can use logistic regression without desperation. If its result is not significant, then it simply means that your "dependent" variable does not depend on the selected predictors. Hope this helps Jan -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Marc Sent: Wednesday, July 26, 2006 8:39 AM To: [hidden email] Subject: Logistic regression, few "respondents", and weighting in SPSS Dear all, I'm coming with a question concerning logistic regression and SPSS. We're in front of a situation here where we have very few "respondents" (1 in the field to predict) in a logistic regression. Only 1700 on 60000. I think it's a situation called "sparsity", isn't it ? When doing a logistic regression, we have a low fit. As I see it, it's because of this sparse dataset. I was told that a way to solve that kind of problem in LR, is to weight the responding cases, to "artificially" raise their representativity in the dataset. I've looked that up in the classical "bibles" of logistic regression (Menard, Lemeshow, Jaccard), but haven't found any discussion of sparsity, or situations with few respondents. In SPPS, I know there is a weight feature. Does it work with logistic regression ? Is it really a "technique" to (artificially) have a better fit ? What modelling techniques are better suited for sparse datasets, in your opinion ? Thank you so much for helping out ! Marc. |
In reply to this post by Marc Feuerstein
You can of course inflate the weight of your cases but it is not a good idea
at all. When the probability of an event is low (1700 on 60000) that's tough luck, but you cannot change it without disfiguring your data. About lack of fit: one thing is lack of fit itself (Nagelkerke too low etc), another is that the classification table does not predict most of the events. The latter is because by default SPSS predicts an event when its probability by logistic regression is over 0.50, which seldom happens when the event is rare. It will probably predict "no event" (0) in all cases, missing all the cases when the events actually happened. On the other hand 1700 cases (or 60000 to be precise) are numerous enough for the results being statistically significant. That is, whatever you find will not be a sample fluke but (with 95% confidence) a true representation of what happens at population level. About weighting see my paper in the tutorials section of www.spsstools.net (go to macros or syntax and then to tutorials). Hector -----Mensaje original----- De: SPSSX(r) Discussion [mailto:[hidden email]] En nombre de Marc Enviado el: Wednesday, July 26, 2006 3:39 AM Para: [hidden email] Asunto: Logistic regression, few "respondents", and weighting in SPSS Dear all, I'm coming with a question concerning logistic regression and SPSS. We're in front of a situation here where we have very few "respondents" (1 in the field to predict) in a logistic regression. Only 1700 on 60000. I think it's a situation called "sparsity", isn't it ? When doing a logistic regression, we have a low fit. As I see it, it's because of this sparse dataset. I was told that a way to solve that kind of problem in LR, is to weight the responding cases, to "artificially" raise their representativity in the dataset. I've looked that up in the classical "bibles" of logistic regression (Menard, Lemeshow, Jaccard), but haven't found any discussion of sparsity, or situations with few respondents. In SPPS, I know there is a weight feature. Does it work with logistic regression ? Is it really a "technique" to (artificially) have a better fit ? What modelling techniques are better suited for sparse datasets, in your opinion ? Thank you so much for helping out ! Marc. |
In reply to this post by Spousta Jan
Have you considered, splitting data into train / test partitions, then
combining all of the respondnts in your training partition with a random samplle of the non-respondents in the same partition? With the model, apply it against the test partition. Jason On 7/26/06, Spousta Jan <[hidden email]> wrote: > Hi Marc, > > >In SPPS, I know there is a weight feature. Does it work with logistic > regression ? > Yes, it works well together. > > >Is it really a "technique" to (artificially) have a better fit ? > Yes, you get a "better" fit, but in a sense it is rather self-deception. > In reality, the fit is still bad and you cannot rely on the results. > > >What modelling techniques are better suited for sparse datasets, in > your opinion ? > SPSS has its exact tests, they are devised for sparse data. Of course, > they cannot create significant results where there is nothing > significant. > > Moreover, I do not understand your phrase "1700 on 60000" (sorry for my > bad English). If it means that you have 60,000 respondents and that 1700 > of them has 1 in the dependent variable and the rest has 0 here, then > the case is not about sparsity. 1700 is enough for most practical > purposes and you can use logistic regression without desperation. If its > result is not significant, then it simply means that your "dependent" > variable does not depend on the selected predictors. > > Hope this helps > > Jan > > -----Original Message----- > From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of > Marc > Sent: Wednesday, July 26, 2006 8:39 AM > To: [hidden email] > Subject: Logistic regression, few "respondents", and weighting in SPSS > > Dear all, > I'm coming with a question concerning logistic regression and SPSS. > > We're in front of a situation here where we have very few "respondents" > (1 in the field to predict) in a logistic regression. Only 1700 on > 60000. I think it's a situation called "sparsity", isn't it ? > > When doing a logistic regression, we have a low fit. As I see it, it's > because of this sparse dataset. I was told that a way to solve that kind > of problem in LR, is to weight the responding cases, to "artificially" > raise their representativity in the dataset. > > I've looked that up in the classical "bibles" of logistic regression > (Menard, Lemeshow, Jaccard), but haven't found any discussion of > sparsity, or situations with few respondents. > > In SPPS, I know there is a weight feature. Does it work with logistic > regression ? Is it really a "technique" to (artificially) have a better > fit ? > > What modelling techniques are better suited for sparse datasets, in your > opinion ? > > Thank you so much for helping out ! > > Marc. > |
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