[R] Newbie question--locally weighted regression

[Gregory Snow]

Using a gam model (package gam, possibly others) will take care of the
link function (and variance function) for you and allow using loess to
fit the data.  Here is a quick example to get you started, though you
should read up on gam models yourself as well.

library(gam)

x <- seq(0,1, length=250)
y <- rpois(250, (sin(x*2*pi)+1.2)*3)

plot(x,y)
lines(x,(sin(x*2*pi)+1.2)*3, col='blue')

fit <- gam(y~lo(x), family=poisson)

lines(x, predict(fit, data.frame(x=x), type='response'), col='green')

fit2 <- gam(y~lo(x, span=0.75, degree=2), family=poisson)
lines(x, predict(fit2, data.frame(x=x), type='response'), col='red')


Hope this helps,


-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at intermountainmail.org
(801) 408-8111
 
 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Thomas L Jones
> Sent: Wednesday, January 04, 2006 2:11 AM
> To: R-project help
> Subject: [R] Newbie question--locally weighted regression
> 
> 
> I have a dataset, a time series comprising count data at five 
> minute intervals. These are the number of people who voted at 
> a particular voting place during a recent election. The next 
> step is to smooth the data and estimate a demand vs 
> time-of-day function; the problem is of interest in 
> preventing long lines at voting places. I am using the R 
> Project software.
> 
> However, I am not a statistician, and I am somewhat baffled 
> by how to do the smoothing. These are integers with roughly 
> Poisson distribution, and the use of a least-squares 
> regression would create large errors. Apparently something 
> called a "link function" factors into the equation somehow.
> 
> Question: Do I want a link function? If so, do I want a 
> logarithmic link function? Unless I change my mind, I will 
> use lowess or loess for the smoothing; how do I tell it to 
> use a link function?
> 
> Doc
> 
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