As I think it is not spam but helpful, let me repeat my stats.stackexchange.com question here, from http://stats.stackexchange.com/questions/16346/difference-between-lp-or-simply-in-rs-locfit
I am not sure I see the difference between different examples for local logistic regression in the documentation of the gold standard locfit package for R: <http://cran.r-project.org/web/packages/locfit/locfit.pdf> I get starkingly different results with fit2<-scb(closed_rule ~ lp(bl),deg=1,xlim=c(0,1),ev=lfgrid(100), family='binomial',alpha=cbind(0,0.3),kern="parm") from fit2<-scb(closed_rule ~ bl,deg=1,xlim=c(0,1),ev=lfgrid(100), family='binomial',alpha=cbind(0,0.3),kern="parm") . What is the nature of the difference? Maybe that can help me phrase which I wanted. I had in mind an index linear in bl within a logistic link function predicting the probability of closed_rule. The documentation of lp says that it fits a local polynomial which is great, but I thought that would happen even if I leave it out. And in any case, the documentation has examples for "local logistic regression" either way [[alternative HTML version deleted]]
______________________________________________ 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.
