R help - Oct 2011 - Difference between ~lp() or simply ~ in R's locfit?

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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