Dear Mathias,
That's pretty amazing, thanks a lot ! I'll have to look all this through
because I don't easily understand why each part has to be set up, in
particular the "initialize method" part seems crucial and is not easy to
intuit. From what I get, the actual name we give to a parameter (my
original "mu" for example) is important in itself, and if we introduce
new variable names we have to define a new generic, right? The simplest
option then is to re-use an existing variable name that has the same
properties/range, right?
Another general question: my actual pdf is of the same type but not the
exact same as the skew normal. In particular, I don't have a rule for
building the slot r (eg the one borrowed from the sn package in your
example); is it a problem? isn't it sufficient to give slot d, and then
you have automatic methods implemented to get from d() to r() slots etc.
is that right?
Thanks a lot for your help and time !
Best,
Guillaume
Matthias Kohl a écrit :
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) obj...@mu)
#I get the following error message:
> Error in setMethod("mu", "SkewNormParameter", function(object)
obj...@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
--
Guillaume MARTIN
Institut des Sciences de l'Evolution de Montpellier
ISEM UMR 5554, Bât. 22 Université Montpellier II
34 090 Montpellier, France
tel: (+33) 4 67 14 32 50
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