Are X1 and X2 both numeric?  You might want to get them on equivalent  
scales, and also play around with the smoothing parameter.

Try something like:

fit <- locfit(Y ~ lp(X1, X2, nn=___, scale=TRUE), family="binomial")

and see what happens for different values of nn (try values between 0  
and 1 and then some larger than one).  I can't be much more help  
without data.


On Jul 27, 2009, at 9:41 PM, cindy Guo wrote:

> Hi, Ryan,
>
> Thank you for the information. I tried it. But there are some error  
> messages.
>
> When I use fit <- locfit(Y~X1*X2,family='binomial'), the error  
> message is
> error lfproc(x, y, weights = weights, cens = cens, base = base, geth  
> = geth,  :
>   compparcomp: parameters out of bounds
>
> And when I use fit <- locfit(Y~X1*X2), the error message is
> error lfproc(x, y, weights = weights, cens = cens, base = base, geth  
> = geth,  :
>   newsplit: out of vertex space
>
> This happens sometimes, not every time for different data. Do you  
> know what's the reason?
>
> Thank you,
>
> Cindy
>
> On Mon, Jul 27, 2009 at 5:25 PM, Ryan <rhafen@purdue.edu> wrote:
> > >
> > > Hi, All,
> > >
> > > I have a dataset with binary response ( 0 and 1) and some  
> numerical
> > > covariates. I know I can use logistic regression to fit the  
> data. But I
> > > want
> > > to consider more locally. So I am wondering how can I fit the  
> data with
> > > 'loess' function in R? And what will be the response: 0/1 or the
> > > probability
> > > in either group like in logistic regression?
> > >
> > > -- Neither. Loess is an algorithm that smoothly "interpolates"  
> the data. It
> > > makes no claim of modeling the probability for a binary response  
> variable.
> > >
> > > -- Bert Gunter
> > > Genentech Nonclinical Statistics
> > >
> > > Thank you,
> > > Cindy
> > >
> > >        [[alternative HTML version deleted]]
> > >
>
> Actually, loess is much more than an "interpolant".  I wouldn't
> even call it that.  It is a local regression  technique that comes
> with all the equipment you get in classical regression.  But it
> is meant for normal-like errors, which is not what you have.
>
> I would recommend that you take a look at the locfit package.
> It fits local likelihood models.  I've never tried it with binary  
> data,
> but if y is your 0/1 response and x is a covariate, you might try
> something like:
>
> locfit(y ~ x, ..., family="binomial")
>
> If you have a good library at your disposal, try picking up Loader's
> book "Local Regression and Likelihood".
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>


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