[R] (no subject)

[Christian Hennig]

Dear Nadja,

if the loglikelihood function has various local maxima, the result
may depend on the starting values. This is not unusual. The best estimator
is the one with the maximum loglikelihood, i.e., the smallest value of
-2 log L in the mle output. (Unfortunately, it seems that the
loglikelihood value is not accessible using fitdistr - you would have to
implement the loglikelihood function on you own.)

You could use a lot of starting values, for example generated by some
random mechanism, and take the best estimator.
If you want a single good starting value, you could try to fit a Weibull
distribution "by eye"  and trial-and error to the histogram and use the
corresponding parameters.

Best,
Christian

PS: Please use informative subject lines.

On Tue, 6 Sep 2005, Nadja Riedwyl wrote:

> my problem actually arised with fitting the data to the weibulldistribution,
> where it is hard to see, if the proposed parameterestimates make sense.
>
> data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491;
>           14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334
>
> how am I supposed to know what starting values i have to take?
> i get different parameterestimates depending on the starting values i choose,
> this shouldn't be, no? how am i supposed to know, which the "right" estimates
> should be?
>
>
> > library(MASS)
> > fitdistr(data2,densfun=dweibull,start=list(scale=2 ,shape=1 ))
>       scale          shape
>   1.378874e+04   8.788857e-01
>  (3.842224e+03) (1.312395e-01)
>
> > fitdistr(data2,densfun=dweibull,start=list(scale=6 ,shape=2 ))
>      scale        shape
>   7.81875000   0.12500000
>  (4.18668905) (0.01803669)
>
> #if i use the lognormaldistribution instead, i would get the same estimates,
> #no matter, what starting values i choose.
>
> #or if i tried it so fare with mle(), i got different values depending on the
> #starting values too, i use the trial and error method to find appropriate
> #starting values, but i am sure, there is a clear way how to do it, no?
> #shouldn't i actually get more or less the same parameterestimates with both
> #methods?
>  library(stats4)
> > ll<-function(alfa,beta)
> + {n<-24
> + x<-data2
> + -n*log(alfa)-n*log(beta)+alfa*sum(x^beta)-(beta-1)*sum(log(x))}
> > est<-mle(minuslog=ll, start=list(alfa=10, beta=1))
> There were 50 or more warnings (use warnings() to see the first 50)
> > summary(est)
> Maximum likelihood estimation
>
> Call:
> mle(minuslogl = ll, start = list(alfa = 10, beta = 1))
>
> Coefficients:
>         Estimate   Std. Error
> alfa 0.002530163 0.0006828505
> beta 0.641873010 0.0333072184
>
> -2 log L: 511.6957
>
> > library(stats4)
> > ll<-function(alfa,beta)
> + {n<-24
> + x<-data2
> + -n*log(alfa)-n*log(beta)+alfa*sum(x^beta)-(beta-1)*sum(log(x))}
> > est<-mle(minuslog=ll, start=list(alfa=5, beta=17))
> There were 50 or more warnings (use warnings() to see the first 50)
> > summary(est)
> Maximum likelihood estimation
>
> Call:
> mle(minuslogl = ll, start = list(alfa = 5, beta = 17))
>
> Coefficients:
>         Estimate  Std. Error
> alfa 0.002143305 0.000378592
> beta 0.660359789 0.026433665
>
> -2 log L: 511.1296
>
>
> thank you very much for all your comments, it really helps me to get further!
> Nadja
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
>

*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche