[R] implementing Maximum Likelihood with distrMod when only the PDF is known

[Matthias Kohl]

Dear Guillaume,

thanks for your interest in the distrMod package.

Regarding your question I took up your example and put a file under:

http://www.stamats.de/distrModExample.R

Hope that helps ...

Don't hesitate to contact me if you have further questions!

Best,
Matthias

guillaume.martin schrieb:
> Dear R users and Dear authors of the distr package and sequels
>
> I am trying to use the (very nice) package distrMod as I want to 
> implement maximum likelihood (ML) fit of some univariate data for 
> which I have derived a theoretical continuous density (pdf). As it is 
> a parametric density, I guess that I should implement myself a new 
> distribution of class AbscontDistributions (as stated in the pdf on 
> "creating new distributions in distr"), and then use MLEstimator() 
> from the distrMod package. Is that correct or is there a simpler way 
> to go? Note that I want to use the distr package because it allows me 
> to implement simply the convolution of my theoretical pdf with some 
> noise distribution that I want to model in the data, this is more 
> difficult with fitdistr or mle.
> It proved rather difficult for me to implement the new class following 
> all the steps provided in the example, so I am asking if someone has 
> an example of code he wrote to implement a parametric distribution 
> from its pdf alone and then used it with MLEstimator().
>
> I am sorry for the post is a bit long but it is a complicate question 
> to me and I am not at all skillful in the handling of such notions as 
> "S4 - class", etc.. so I am a bit lost here..
>
> As a simple example, suppose my theoretical pdf is the skew normal 
> distribution (available in package sn):
>
> #skew normal pdf (default values = the standard normal N(0,1)
>
> fsn<-function(x,mu=0,sd=1,d=0) {u = (x-mu)/sd;  f = 
> dnorm(u)*pnorm(d*u); return(f/sd)}
>
> # d = shape parameter (any real), mu = location (any real), sd = scale 
> (positive real)
>
> #to see what it looks like try
> x<-seq(-1,4,length=200);plot(fsn(x,d=3),type="l")
>
> #Now I tried to create the classes "SkewNorm" and "SkewNormParameter" 
> copying the example for the binomial
> ##Class:parameters
> setClass("SkewNormParameter",
> representation=representation(mu="numeric",sd="numeric",d="numeric"),
> prototype=prototype(mu=0,sd=1,d=0,name=gettext("Parameter of the Skew 
> Normal distribution")),
> contains="Parameter"
> )
>
> ##Class: distribution (created using the pdf of the skew normal 
> defined above)
> setClass("SkewNorm",prototype = prototype(
>     d = function(x, log = FALSE){fsn(x, mu=0, sd=1,d=0)},
>     param = new("SkewNormParameter"),
>     .logExact = TRUE,.lowerExact = TRUE),
> contains = "AbscontDistribution"
> )
>
> #so far so good but then with
> setMethod("mu", "SkewNormParameter", function(object) object at mu)
>
> #I get the following error message:
>
> > Error in setMethod("mu", "SkewNormParameter", function(object) 
> object at mu) : no existing definition for function "mu"
>
> I don't understand because to me mu is a parameter not a function... 
> maybe that is too complex programming for me and I should switch to 
> implementing my likelihood by hand with numerical convolutions and 
> optim() etc., but I would like to know how to use distr, so if there 
> is anyone who had the same problem and solved it, I would be very 
> grateful for the hint !
>
> All the best,
> Guillaume
>
>
>

-- 
Dr. Matthias Kohl
www.stamats.de