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@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