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
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