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

[hidden email]
(801) 408-8111

> -----Original Message-----

> From:

[hidden email]
> [mailto:

[hidden email]] 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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