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 <ripley@stats.ox.ac.uk>:

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.

R-help at stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help--

Brian D. Ripley, ripley at stats.ox.ac.uk

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

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:

Function cannot be evaluated at initial parameters

shape1 shape2

0.08728921 0.10403875

(0.01044863) (0.01320188)

hope this helps. spencer graves

Prof Brian Ripley wrote:

henceMy standard work-around for the kind of problem you identified is to

shrink the numbers just a little towards 0.5. For example:

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, ...) :a <- rbeta(100,0.1,0.1)

fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))

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))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",

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

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:

______________________________________________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:

(0.01120670) (0.01550129)

but sometimes does not:

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

alobo at ija.csic.es

______________________________________________

R-help at stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help

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 0.12048753fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1)

shape1 shape2

(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 parametersfitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1)

Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) :

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

alobo at ija.csic.es

______________________________________________

R-help at stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help

R-help at stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help

Brian D. Ripley, ripley at stats.ox.ac.uk

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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