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…
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