[R-sig-finance] Distribution Fitting

[Martin Maechler]

>>>>> "Lorenzo" == Lorenzo Isella <lorenzo.isella at gmail.com>
>>>>>     on Mon, 24 Apr 2006 00:10:41 +0200 writes:

    Lorenzo> Dear All,
    Lorenzo> I am experiencing some problems in fitting a trimodal distribution (which
    Lorenzo> should be thought as a sum of three Gaussian distributions) using the nls
    Lorenzo> function for nonlinear fittings.
    Lorenzo> As a test, just consider the very simple code:

    Lorenzo> rm(list=ls());
    Lorenzo> mydata<-rnorm(10000,0,4);
    Lorenzo> mydens<-density(mydata,kernel="gaussian");
    Lorenzo> y1<-mydens$y;
    Lorenzo> x1<-mydens$x;
    Lorenzo> myfit<-nls(y1~A*exp(-x1^2/sig));

(it's slightly inefficient and completely unnecessary to append
 an empty statement to every line -- which you 
 do incidentally if you add superfluous ';' at end of lines )


    Lorenzo> which I use to get the empirical density (as I would from real experimental
    Lorenzo> data) and test it against a Gaussian ansatz.
    Lorenzo> Well, either R always crashes for a segmentation fault and I have to restart
    Lorenzo> it manually or I get this output:

    Lorenzo> Error in match.call(definition, call, expand.dots) :
    Lorenzo> '.Primitiv�i�d�������������
...' is not a function

I cannot confirm any segmentation fault (and if you really get
them, indeed your R *installation* is buggy (or outdated)), but
indeed, you've revealed a buglet in nls() - which is still
present in R 2.3.0 which has been released a few hours ago.
Thank you for reporting it as a reproducible example!

I'm going to try to fix the bug.

    Lorenzo> Am I missing the obvious or is there some bug in my R build?

As others have said (implicitly): you're missing the facts

-  that it's rather bad idea to fit "data" to a density estimate.
-  if your density has a closed from, you should rather use
   maximum likelihood, typically most easily  via MASS::fitdistr

Martin