Hallo, it seems as if something did not work with my first email
I would like to estimate parameters of a general extreme value (GEV) distribution using maximum likelihood as implemented in the gev.fit function of package ismev. If I do the follwing: y.training<- c(22, 22, 18, 19, 18, 18, 22, 27, 25, 19, 18, 21, 18, 20, 18, 19, 18, 21, 29, 18, 22, 19, 19, 24, 18, 21, 22, 20, 20, 27, 18, 20, 20, 18, 18, 18, 21, 18, 18, 21, 26, 19, 18, 19, 19, 18, 19, 18, 20, 20, 25, 21, 26, 22, 20, 19, 22, 21, 21, 20, 20, 19, 18, 22, 22, 27, 19, 20, 26, 29, 18, 20, 19, 22, 23, 18, 20, 20, 22, 18, 23, 18, 20, 19, 27, 21, 22, 18, 18, 19, 18, 21, 18, 23, 18, 18, 20, 20, 24, 19, 18, 19, 19, 23, 19, 18, 25, 18, 24, 19) fit<-gev.fit(xdat=y.training,show=F) round(fit$mle,2) # 18.00 , 0.00 , 3.96 # The estimated shape parameter is 3.96. I would like to perform the estimation under the constraint that the shape parameter is smaller than 1, but the following does not work: fit<-gev.fit(xdat=y.training,show=F,method="L-BFGS-B",lower=c(0,0,-2),upper=c(50,10,1)) round(fit$mle,2) # 18.09 , 0.27 , 3.05 It seems as is the "lower" and "upper" values are not passed to the optim function in the way they should be. A warning says that they are only passed to the "control" part of optim. Therefore my question: (How) is it possible to use the gev.fit-function to perform the ML estimation under the constraint that the shape parameter is smaller than 1? Or more general: Is it possible to use the gev.fit function under box constraints as it should be possible for optim? Thanks in advance. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
