Why not just use the "deriv" or "D" functions in R? I think for
non-parametric models, as long as the functions are smooth and
continuous over the interval you're looking at, R will be able to
numerically differentiate the function.

On Fri, 2010-04-02 at 01:36 -0700, FMH wrote:

> Dear All,
> 
> I've been searching for appropriate codes to compute the rate of change and the curvature of  nonparametric regression model whish was denoted by a smooth function but unfortunately don't manage to do it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators.
> 
> The following are the example of fitting a nonparametric regression model via smoothing spline function from the Help file in R.
> 
> #######################################################
> attach(cars)
> plot(speed, dist, main = "data(cars)  &  smoothing splines")
> cars.spl <- smooth.spline(speed, dist)
> lines(cars.spl, col = "blue")
> lines(smooth.spline(speed, dist, df=10), lty=2, col = "red")
> legend(5,120,c(paste("default [C.V.] => df =",round(cars.spl$df,1)),"s( * , df = 10)"), col = c("blue","red"), lty = 1:2, bg='bisque')
> detach()
> 
> #######################################################
> 
> 
> Could someone please advice me the appropriate way to determine such derivatives on the curves which were fitted by the function above and would like to thank you in advance.
> 
> Cheers
> Fir 
> 
> 
> 
> 
> 
> _______________________________________________
> R-SIG-Finance@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R questions should go.
> 



	[[alternative HTML version deleted]]

