The glm function allows you to specify starting values for beta (start =), eta (etastart =) or mu (mustart =). In your case the starting value for beta would seem to be the most appropriate. You might find providing a starting value for mu or eta also works well, provided you can come up with a quick way of finding values for the new observations.
________________________________________ From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of useR [milicic.ma...@gmail.com] Sent: 07 June 2009 21:20 To: r-help@r-project.org Subject: [R] One rather theoretical question about fitting algorithm Hi, What I'm trying to achieve is very fast algorithm for fitting logistic regression model. I have to estimate regression coeficients using about 10k observations. Once I have coefficients estimated, new 100 rows of data becomes available.... Now I need to reestimate coeficients using 100 newly arrived observations and removing 100 oldest observations. So, my question is would it be possible to somehow reuse pre iusly calculated coeficients and only adjust them cor newly arrived data? I know it would have to be some aproximation but I suppose it will be good enough. I dont mind doing this in straight C because of of speed perative. Actualy this will have to be cAlculated in a fraction of second. Any ideas would be higly appreciated ______________________________________________ R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.
