# Difference between ~lp() or simply ~ in R's locfit [closed]

(Please note the cross-post at http://stackoverflow.com/questions/7626347/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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## closed as off topic by chl♦Nov 11 '11 at 12:16

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Please, don't cross-post. Choose one site once and for all. I will delete this one by the end of the week without action from your part. – chl Nov 9 '11 at 22:23