(no subject)

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