If Ted is right, then one work-around is to use Firth's method for penalized log-likelihood. The technique is originally intended to reduce small sample bias. However, it's now being extended to deal with complete and quasi separation problems.
I believe the library is called logistf but I haven't had a chance to try it....I know the SAS version (called the fl macro) works fine. Reference -- http://www.meduniwien.ac.at/user/georg.heinze/techreps/tr2_2004.pdf Hope this helps, Jeff Miller University of Florida AlphaPoint05, Inc. -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Ted Harding Sent: Thursday, March 15, 2007 2:39 PM To: R-help Subject: Re: [R] logistic regression On 15-Mar-07 17:03:50, Milton Cezar Ribeiro wrote: > Dear All, > > I would like adjust and know the "R2" of following presence/absence > data: > > x<-1:10 > y<-c(0,0,0,0,0,0,0,1,1,1) > > I tryed use clogit (survival package) but it don´t worked. > > Any idea? > > miltinho You are trying to fit an equation P[y = 1 ; x] = exp((x-a)/b))/(1 + exp((x-a)/b)) to data x = 1 2 3 4 5 6 7 8 9 10 y = 0 0 0 0 0 0 0 1 1 1 by what amounts to a maximum-likelihood method, i.e. which chooses the parameter values to maximize the probability of the observed values of y (given the values of x). The maximum probability possible is 1, so if you can find parameters which make P[y = 1] = 0 for x = 1, 2, ... , 7 and P[y = 1] for x = 8, 9, 10 then you have done it. This will be approximated as closely as you please for any value of a between 7 and 8, and sufficiently small values of b, since for such parameter values P[y = 1 ; x] -> 0 for x < a, and -> 1 for x > a. You therefore have a solution which is both indeterminate (any a such that 7 < a < 8) and singular (b -> 0). So it will defeat standard estimation methods. That is the source of your problem. In a more general context, this is an instance of the "linear separation" problem in logistic regression (and similar methods, such a probit analysis). Basically, this situation implies that, according to the data, there is a perfect prediction for the results. There is no well-defined way of dealing with it; any approach starts from the proposition "this perfect prediction is not a reasonable result in the context of my data", and continues by following up what you think should be meant by "not a reasonable result". What this is likely to mean would be on the lines of "b should not be that small", which then imposes upon you the need to be more specific about how small b may reasonably be. Then carry on from there (perhaps by fixing the value of b at different reasonable levels, and simply fitting a for each value of b). Hoping this helps ... but I'm wondering how it happens that you have such data ... ?? best wishes, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <[EMAIL PROTECTED]> Fax-to-email: +44 (0)870 094 0861 Date: 15-Mar-07 Time: 19:38:51 ------------------------------ XFMail ------------------------------ ______________________________________________ [email protected] mailing list 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. ______________________________________________ [email protected] mailing list 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.
