R help - Jul 2011 - LOESS function Newton optimization

I have a question about running an optimization function on an existing LOESS
function defined in R. I have a very large dataset (1 million observations)
and have run a LOESS regression. Now, I want to run a Newton-Raphson
optimization to determine the point at which the slope change is the
greatest.

I am relatively new to R and have tried several permutations of the maxNR
and nlm functions with no success. For example, I used the nlm function as
follows:

LOESS <- loess(Y ~ X)
optim <- nlm(function(x) LOESS(x))...

However, this doesn't seem to work. In the examples I see online, the
function in nlm and maxNR are user defined, instead of the output of another
function (i.e., my LOESS regression).

Is it possible to run this type of optimization function on my Loess
function?

Thanks in advance for your help.

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On Jul 12, 2011, at 7:53 PM, KenjiPsyD wrote:
> I have a question about running an optimization function on an  
> existing LOESS
> function defined in R. I have a very large dataset (1 million  
> observations)
> and have run a LOESS regression. Now, I want to run a Newton-Raphson
> optimization to determine the point at which the slope change is the
> greatest.
>
> I am relatively new to R and have tried several permutations of the  
> maxNR
> and nlm functions with no success. For example, I used the nlm  
> function as
> follows:
>
> LOESS <- loess(Y ~ X)
> optim <- nlm(function(x) LOESS(x))...
I don't see how that would be examining slopes.
>
> However, this doesn't seem to work. In the examples I see online, the
> function in nlm and maxNR are user defined, instead of the output of  
> another
> function (i.e., my LOESS regression).
>
> Is it possible to run this type of optimization function on my Loess
> function?
 > cars.lo <- loess(dist ~ speed, cars)
 > is.function( cars.lo)
[1] FALSE

If you want to make it a function, there is approxfun in the stats  
package.

car.fun <- approxfun(x=cars.lo$x, cars.lo$fitted)
plot(dist~speed, data=cars)
curve(car.fun, add=TRUE)

Some functions require that a function be offered that takes x as its  
argument.

 > car.fun(x=5)
Error in car.fun(x = 5) : unused argument(s) (x = 5)

If that is the case with your optimization routine then you can create  
one with:

 > car.funx <- function(x) car.fun(x)
 > car.funx(x=5)
[1] 8.095681

-- 

David Winsemius, MD
West Hartford, CT