I am not at all familiar with that distribution but the obvious
solution would appear to be:
?integrate
> BetaprimeDensity <- function(x) x^(shape1-1) * (1+x)^(-shape1-
shape2) / beta(shape1,shape2)
> shape1 <- 1
> shape2 <-1
> integrate(BetaprimeDensity, 0 , 1)
0.5 with absolute error < 5.6e-15
If I knew more about that distribution I would be able to see if that
were sensible, but severe caveats apply, since I just took you
expression and massaged it into a function. If I knew what the domain
was supposed to be, it would be simple matter to wrap that expression
into a function that could be called pbetaprime. Perhaps:
> pbetaprime <- function(ul, ll=0, s1=2, s2=2) integrate(function(x)
{x^(s1-1) * (1+x)^(-s1-s2) / beta(s1,s2)}, ll, ul)
> pbetaprime(5, , 5, 3)
0.9042245 with absolute error < 7e-05
Does that "work" when given the proper sequence vector of values and
submitted to plot?
--
DW
On Jul 1, 2009, at 3:19 AM, aledanda wrote:
Hallo,
I need your help.
I fitted my distribution of data with beta-prime, I need now to plot
the
Cumulative distribution. For other distribution like Gamma is easy:
x <- seq (0, 100, 0.5)
plot(x,pgamma(x, shape, scale), type= "l", col="red")
but what about beta-prime? In R it exists only pbeta which is
intended only
for the beta distribution (not beta-prime)
This is what I used for the estimation of the parameters:
mleBetaprime <- function(x,start=c(1,1)) {
mle.Estim <- function(par) {
shape1 <- par[1]
shape2 <- par[2]
BetaprimeDensity <- NULL
for(i in 1:length(posT))
BetaprimeDensity[i] <- posT[i]^(shape1-1) *
(1+posT[i])^(-shape1-shape2) / beta(shape1,shape2)
return(-sum(log(BetaprimeDensity)))
}
est <- optim(fn=mle.Estim,par=start,method="Nelder-Mead")
return(list(shape1=est$par[1],shape2=est$par[2]))
}
posbeta1par <- fdp(posT, family= "beta1")
Hope you can help me.
Thanks a lot!!!
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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