Dear Lorenzo,
Please do read the posting guide (https://www.r-project.org/posting-guide.html)
: The ‘main’ R mailing list, for discussion about problems and solutions using
R, about the availability of new functionality for R and documentation of R,
comparison and compatibility with S-plus, and for the posting of nice examples
and benchmarks.
For your problem, you could do something like this
x <- rbeta(1000, 3, 3)
dbeta2 <- function(x, shape, ...)
dbeta(x, shape, shape, ...)
pbeta2 <- function(q, shape, ...)
pbeta(q, shape, shape, ...)
library(fitdistrplus)
fitdist(x, "beta2", start=list(shape=1/2))
x <- rbeta(1000, .3, .3)
fitdist(x, "beta2", start=list(shape=1/2), optim.method="L-BFGS-B", lower=1e-2)
Regards, Christophe
---------------------------------------
Christophe Dutang
LMM, UdM, Le Mans, France
web: http://dutangc.free.fr
> Le 23 mai 2017 à 18:22, Lorenzo Isella <lorenzo.ise...@gmail.com> a écrit :
>
> Dear All,
> In principle it is a simple question, but not idea about how to tackle
> it.
> Suppose you have a distribution depending on two parameters,
> e.g. beta(a,b).
> For some reasons, you want to impose
> that the two parameters of the beta distribution are identical,
> i.e. you want to fit your data to beta(a,a).
> For instance
>
> x<-rbeta(1000, 0.1, 0.1)
> require(fitdistrplus)
>
> mm<-fitdist(x, "beta", method = "mge")
>
>
> is how you would normally fit a sample, but I do not know how to
> impose the constrain a=b before the fitting.
> Any suggestion is appreciated.
> Cheers
>
> Lorenzo
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