The "local regression" in locfit _is_ local polynomial. The "local

polynomial" you usually see are fixed bandwidth smoothers (e.g., locpoly in

KernSmooth). loess() also does bivariate smoothing, and it is fitting

polynomials locally, but instead of using fixed bandwidth, it uses a fixed

proportion of the data. locfit() by default does the same thing (that's

what alpha is for, if given as a single number). However, locfit() can do

other things as well: If you specify alpha=c(b1, b2), then b1 is ignored

and b2 is taken as the fixed bandwidth to use. locfit() can even do locally

adaptive bandwidth choice (if you specify acri). The scale argument

specifies whether the predictor variables are to be scaled by their marginal

SD. See the help page for locfit.raw() for details.

If you really want to know more, Prof. Loader's book (for which the package

is support software) is indispensable.

Andy

From: yyan liu

>

> Hi:

> I am using the package "KernSmooth" to do the local

> polynomial regression. However, it seems the function

> "locpoly" can only deal with univariate covaraite. I wonder

> is there any kernel smoothing package in R can deal with

> bivariate covariates? I also checked the package "lcofit" in

> which function "lcofit" can indeed deal with bivariate case.

> The code below is an example from the its help document I

> found on

http://www.locfit.info However, first, it is a

> local regression method. I am not sure how to specify the

> degree of the regression model. second, i dont know what are

> "scale" and "alpha". Are they associated with the bandwidth?

> Thanks very much!

>

>

>

> data(ethanol)

>

> # a bivariate local regression with smaller smoothing

> parameter fit <- locfit(NOx~E+C, data=ethanol, scale=0, alpha=0.5)

> plot(fit)

>

>

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>

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