|Date: ||Thu, 1 Apr 1999 17:08:45 -0600|
|Reply-To: ||"Nichols, David" <nichols@SPSS.COM>|
|Sender: ||"SPSSX(r) Discussion" <SPSSX-L@UGA.CC.UGA.EDU>|
|From: ||"Nichols, David" <nichols@SPSS.COM>|
|Subject: ||Re: ordinal logistic regression|
A perfect fit means no error at all. All 1 responses are predicted to be
exactly 1, and all 0 responses exactly 0. When it occurs, there will be no
unique set of parameter values that will produce the perfect fit.
Principal Support Statistician and
Manager of Statistical Support
> -----Original Message-----
> From: KB [SMTP:cg815@FREENET.TORONTO.ON.CA]
> Sent: Friday, March 12, 1999 3:40 PM
> To: SPSSX-L@UGA.CC.UGA.EDU
> Subject: Re: ordinal logistic regression
> Hi John,
> I read your explanations in the news group about the logistic question and
> have a question for you :
> I am having some troubles to understand what is happening with a
> Let me explain it to you and if you can give me the answer it would help
> in my researches.
> I am working on a project involving different classification and
> optimization techniques.
> In order to demonstrate a theory we are working on, my supervisor and I,
> are using some dataset published in the literature.
> Therefore, we agreed to use a simple dataset in the banking industry to
> on the logistic regression feature and extract some results.
> This dataset has 5 independent variables (x1, ..., x5) and a dependent
> variable Y which can take the values of 0 and 1 (0 for bankrupt banks and
> for solvent ones). This dataset is based on a sample of 66 observations.
> Here is for your info the meaning of each variable:
> x1 = (working capital) / (total assets)
> x2 = (retained earnings) / (total assets)
> x3 = (earnings before interest and taxes) / (total assets)
> x4 = (market-value equity) / (book value of total liabilities)
> x5 = sales / (total assets)
> y = 0 if bankrupt after 2 years (33 firms)
> y = 1 if solvent after 2 years (33 firms)
> The paper says:
> "A multiple logistic regression model was fitted using variables x2 and
> The other thre variables did not substantially add to the explanatory
> of the model. The fitte logistic model is:
> g(X) = ln( P/(1-P)) = -0.550 + 0.157x2 + 0.194x3
> the predicted probabilities Phat(X) for the remaining solvent is given by
> Phat(x) = exp(g(x)) / [1 + exp(g(x))]
> .... if we classify any firm with Phat(X) greater than 0.5 to be a solvent
> firm, the model given above misclassifies only two firms, one from the
> bankrupt and one from the solvent category...."
> Well, I used the same hypothesis and tried the logistic regression with
> SPSS. Here is the options I chose :
> Input file : bkfirm.sav (the attached file)
> Statistics ---> Regression ---> Logistic
> Dependent variable : Y
> Covariates : X1, ...., X5
> Options : Classification plots, Correlation of estimates, Iteration
> Hsmer-Lemeshow goodness-of-fit, 99 iterations.
> First, I tried it with the two variables X2 and X3 and I found exactly the
> same results. That was great !
> Then I tried to do it with the five variables and SPSS prompted me the
> following message (cf.
> bkfirm2.spo) :
> "Estimation terminated at iteration number 25 because
> a perfect fit is detected. This solution is not unique."
> at the end of the output it couldn't compute the logistic parameters and
> prompted the following message instead:
> ">Warning # 18582
> >Covariance matrix cannot be computed. Remaining statistics will be
> May be you have an idea about what's happening and how I can handle that
> kind of problem.
> My ultimate goal is to run many logistic regressions on the same dataset
> moving firms from 0 to 1 and vice versa.
> About 100 simulations would be a first step and it will help to validate
> some predicting theory based on other techniques such as goal programming
> and DEA.
> Thanks for your help.
> J.Hendrickx a icrit dans le message ...
> >In article <email@example.com>, firstname.lastname@example.org says...
> >> Can I run an ordinal logistic regression in SPSS? If so, how? I don't
> >> too many options; a logit link function will do. I am running the most
> >> recent version of SPSS.
> >SPSS version 9 can run multinomial logistic regression but apparently not
> >ordered logistic regression (I don't have version 9 yet, it always takes
> >half a year longer here before a new release is available, so I can't say
> >for certain). I have a macro that uses MATRIX to estimate an ordered
> >logistic model; it will run on any version of SPSS. Get OLOGIT2.INC at
> >SPSS is offering a separate program GOLDMINER for ordered logistic
> >regression. See http://www.spss.com/software/goldminer/ for more
> >information. There's a demo available and a paper with a description of
> >the model under the "White Papers" tab at this site.
> >However, goldminer is not a proportional odds cumulative logistic
> >regression model. It's referred to in the white paper mentioned above as
> >a "parallel log-odds" model. What it does is impose a linear restriction
> >on a multinomial logistic model. The model has 3 intercept parameters for
> >a dependent variable with 4 categories, but each independent variable has
> >only one parameter. This indicates the effect of a unit's increase of the
> >independent on the logit for a category of the dependent versus the
> >previous category, which is equal for all adjacent categories.
> >I was disappointed by this model, since it can already be estimated using
> >COXREG.COXREG can be used to estimate a multinomial logistic or
> >conditional logit model, where the latter is actually a more flexible
> >form of the former. See
> >http://baserv.uci.kun.nl/~johnh/mlogist/mlogist.html#The person/choice
> >for details. In a conditional logit specification of the model, it is
> >fairly simple to impose different response functions, i.e. the type of
> >restriction imposed on the dependent variable.
> >The following program will reproduce the results in
> >http://www.spss.com/software/goldminer/biomedical.htm, except that the
> >effects have opposite signs due to a different order of the dependent
> >variable. Not super user-friendly, I admit, but very flexible. The point
> >is that this type of model should have been an option in the new
> >multinomial logistic program.
> >Goldminer would be more worthwhile if it could estimate a proportional
> >odds model as well as this parallel odds model. An even more worthwhile
> >improvement would be to implement a Stereotyped Ordered Regression (SOR)
> >model. A SOR model estimates a scale for the dependent variable based on
> >the effects of the independent variables. So it's basically a parametric
> >version of CATREG in SPSS categories. See Hendrickx & Ganzeboom,
> >"Occupational status attainment in the Netherlands", European
> >Sociological Review 14: 387-403 for an application of this model.
> >Good luck,
> >John Hendrickx
> >SPSS syntax for estimating a parallel odds model.
> >data list free /mental ses events.
> >var labels mental 'Mental Impairment'
> > /ses 'socioeconomic status'
> > /events 'Life Events'.
> >value labels mental 1 'Well' 2 'Mild' 3 'Moderate' 4 'Impaired'
> > /ses 0 'low' 1 'high'.
> >* Agresti 1990: 325.
> >begin data.
> >1 1 1
> >1 1 9
> >1 1 4
> >1 1 3
> >1 0 2
> >1 1 0
> >1 0 1
> >1 1 3
> >1 1 3
> >1 1 7
> >1 0 1
> >1 0 2
> >2 1 5
> >2 0 6
> >2 1 3
> >2 0 1
> >2 1 8
> >2 1 2
> >2 0 5
> >2 1 5
> >2 1 9
> >2 0 3
> >2 1 3
> >2 1 1
> >3 0 0
> >3 1 4
> >3 0 3
> >3 0 9
> >3 1 6
> >3 0 4
> >3 0 3
> >4 1 8
> >4 1 2
> >4 1 7
> >4 0 5
> >4 0 4
> >4 0 4
> >4 1 8
> >4 0 8
> >4 0 9
> >end data.
> >crosstabs events ses by mental /statistics=chisq.
> >* Transform the data to a person-choice file for estimation as a
> >* logit model.
> >* Each respondent has 4 records, one for each category of mental
> >* The dependent variable K has the value 1 for the record corresponding
> >* the respondent's score on mental and 2 for the other records.
> >* A stratifying variable RESP indicates respondents, a copy of K is used
> >* indicate the censored/not censored status of records in COXREG.
> >compute resp=$casenum.
> >loop Y=1 to 4.
> >xsave outfile='%temp%\temp.sav'.
> >end loop.
> >get file='%temp%\temp.sav'.
> >compute K=2.
> >if (mental=Y) K=1.
> >compute cens=K.
> >compute mental=Y.
> >compute mentlin=mental.
> >* Estimate the conditional logit model using coxreg.
> >* The first model is an intercept model, the second uses linear logits
> >* as the response function for events and ses.
> >* The L^2 and df are the same as Goldminer, the effects of events and ses
> >* the same values but opposite signs.
> >coxreg K
> > /status=cens(1)
> > /strata=resp
> > /contrast(mental)=indicator(1)
> > /method=enter mental
> > /method=enter mental mentlin*events mentlin*ses.
> << File: bkrpfirm.sav >> << File: bkfrim2.spo >>