Dear Guillaume,
retrospectively, I'm not sure if the decision to have spezial initialize
methods is the optimal way to go. In distrMod and our other packages on
robust statistics we don't introduce special initialize methods, but use
so-called generating functions. This approach has the advantage that the
default initialize method can be used for programming where I find it
very useful.
I would say it is the canonical (and recommended) way to first define a
function as generic (like mu) and then implement methods for it.
Choosing names for which there is already an existing definition for a
generic function may also not be what you want in general. By defining
new methods you have to respect the definition of the generic function;
that is, your method definition has to be with respect to the arguments
in the given generic function and also dispatching will be on these
arguments (cf. scale in the distrMod example).
In defining our classes we decided that at least the slot "r" has to be
filled (of class "function") whereas d, p and q are of class
"OptionalFunction". Hence, there are functions to fill d, p and q for
given r but, so far not for filling r, p and q given d.
A way to avoid implementing r is given in
http://www.stamats.de/distrModExample1.R
I also do not fill the slots p and q in this example. This avoids the
simulation of a large random sample and speeds up the computation of the
MLE.
However, this is rather a quick and dirty solution and it would of
course be better to have a valid definition for r, d, p and q.
Best,
Matthias
guillaume.martin schrieb:
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
--
Dr. Matthias Kohl
www.stamats.de
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