> a <- rbeta(100,0.1,0.1)
> fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))
Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) :
Function cannot be evaluated at initial parameters
>
> range(a) - (0:1)
[1] 9.62532e-20 0.00000e+00
> sum(a==1)
[1] 1
>
> a1 <- a
> a1[a==0] <- .Machine$double.eps
> a1[a==1] <- (1-.Machine$double.eps)
> fitdistr(x=a1, "beta", start=list(shape1=0.1,shape2=0.1))
shape1 shape2
0.087937990 0.081524037
(0.010950667) (0.009899447)You could also program your own "densfun" argument to "fitdistr" for a mixture of a discrete distribution with point masses at 0 and 1 with the rest following a standard beta distribution. However, I would not recommend that for what I understand of your application.
hope this helps. spencer graves
[EMAIL PROTECTED] wrote:
Thanks Prof. B. Ripley and Spencer Graves for your help.
According to the last message from BR, perhaps I should use another
distribution or combination
of distributions to model my data,
which look like a beta(0.1,0.1)
but can have both 0 and 1. Any
suggestion?
(regarding the extra "1)" in my original message, it was just a pasting problem.)
Agus
Mensaje citado por Prof Brian Ripley <[EMAIL PROTECTED]>:
In this example shrinking by (1 - 2e-16) leads to a significant change in the distribution: see my probability calculation. And you can't shrink by much less. A beta(0.1, 0.1) is barely a continuous distribution.
On Fri, 4 Jul 2003, Spencer Graves wrote:
My standard work-around for the kind of problem you identified is to shrink the numbers just a little towards 0.5. For example:hence
> library(MASS) > a <- rbeta(100,0.1,0.1) > fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1)) Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : Function cannot be evaluated at initial parameters
> r.a <- range(a)
> c0 <- 0
> c1 <- 1
> if(r.a[1]==0)c0 <- min(a[a>0])
> if(r.a[2]==1)c1 <- max(a[a<1])
> c. <- c(c0, 1-c1)
> if(any(c.>0))c. <- min(c.[c.>0])
> c.
[1] 2.509104e-14
> fitdistr(x=0.5*c.[1] + (1-c.[1])*a, "beta", start=list(shape1=0.1,shape2=0.1))
shape1 shape2
0.08728921 0.10403875
(0.01044863) (0.01320188)
>
hope this helps. spencer graves
Prof Brian Ripley wrote:
rbeta(100,0.1,0.1) is generating samples which contain 1, an impossible value for a beta and hence the sample has an infinite log-likelihood.
It is clearly documented on the help page that the range is 0 < x < 1.
However, that is not so surprising as P(X > 1-1e-16) is about 1% and
values will get rounded to one.
The same would happen for a value of 0.
Your code is syntactically incorrect, at least as received here.
On Fri, 4 Jul 2003, Agustin Lobo wrote:
I have the following problem:
I have a vector x of data (0<x<=1 ) with a U-shaped histogram and try to fit a beta distribution using fitdistr. In fact, hist(rbeta(100,0.1,0.1)) looks a lot like my data.
The equivalent to the example in the manual sometimes work:
a <- rbeta(100,0.1,0.1) fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1) shape1 shape2
0.09444627 0.12048753 (0.01120670) (0.01550129)
but sometimes does not:
a <- rbeta(100,0.1,0.1) fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1) Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) :
Function cannot be evaluated at initial parameters
Unfortunately, my data fall in the second case
I've searched for any weird value that be present in the cases in which fitdistr exits with the error message, but could not find any.
Any help? (please if anyone answers be sure to answer to my address as well, I cannot subscribe to the list)
Thanks
Agus
Dr. Agustin Lobo Instituto de Ciencias de la Tierra (CSIC) Lluis Sole Sabaris s/n 08028 Barcelona SPAIN tel 34 93409 5410 fax 34 93411 0012 [EMAIL PROTECTED]
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-- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
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