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Begin forwarded message: > From: Maram SAlem <marammagdysa...@gmail.com> > Date: July 21, 2015 at 11:40:56 PM GMT+2 > To: Arne Henningsen <arne.henning...@gmail.com> > Cc: "r-help@r-project.org" <r-help@r-project.org> > Subject: Re: [R] Warning message with maxLik() > > Dear Arne, > > The elements of the theta vector are indeed strictly positive. I've just > tried to use instead : lamda = log (theta), which means that theta = exp > (lamda), so as to get rid of the log() function that appears in the > log-likelihood and is causing the 50 warnings, but still the estimates I got > for lamda and then those I got for theta (using theta=exp(lamda)) are > irrelvant and their standard errors are infinite, which means that therer is > still a problem that I can't yet figure out. > > Thanks, > Maram > >> On 18 July 2015 at 08:01, Arne Henningsen <arne.henning...@gmail.com> wrote: >> Dear Maram >> >> - Please do not start a new thread for the same issue but reply to >> previous messages in this thread [1]. >> >> - Please read my previous responses [1] more carefully, e.g. to use >> "theta <- exp( param )" which guarantees that all elements of "theta" >> are always positive. >> >> [1] >> http://r.789695.n4.nabble.com/NaN-produced-from-log-with-positive-input-td4709463.html >> >> Best regards, >> Arne >> >> >> >> 2015-07-18 2:46 GMT+02:00 Maram SAlem <marammagdysa...@gmail.com>: >> > Dear All, >> > I'm trying to get the MLe for a certain distribution using maxLik () >> > function. I wrote the log-likelihood function as follows: >> > theta <-vector(mode = "numeric", length = 3) >> > r<- 17 >> > n <-30 >> > >> > T<-c(7.048,0.743,2.404,1.374,2.233,1.52,23.531,5.182,4.502,1.362,1.15,1.86,1.692,11.659,1.631,2.212,5.451) >> > C<- >> > c(0.562,5.69,12.603,3.999,6.156,4.004,5.248,4.878,7.122,17.069,23.996,1.538,7.792) >> > # The loglik. func. >> > loglik <- function(param) { >> > theta[1]<- param[1] >> > theta[2]<- param[2] >> > theta[3]<- param[3] >> > >> > l<-(r*log(theta[3]))+(r*log(theta[1]+theta[2]))+(n*theta[3]*log(theta[1]))+(n*theta[3]*log(theta[2]))+ >> > (-1*(theta[3]+1))*sum(log((T*(theta[1]+theta[2]))+(theta[1]*theta[2])))+ >> > (-1*theta[3]*sum(log((C*(theta[1]+theta[2]))+(theta[1]*theta[2])))) >> > return(l) >> > } >> > >> > then, I evaluated it at theta<- c(40,50,2) >> > >> > v<-loglik(param=theta) >> > v >> > [1] -56.66653 >> > >> > I used this same log-likelihood function, once with analytic gradient and >> > another time with numerical one, with the maxLik function, and in both >> > cases I got the same 50 warning messages and an MLE which is completely >> > unrealistic as per my applied example. >> > >> > a <- maxLik(loglik, gradlik, hesslik, start=c(40,50,2)) >> > >> > where gradlik and hesslik are the analytic gradient and Hessian matrix, >> > respectively, given by: >> > >> > U <- vector(mode="numeric",length=3) >> > gradlik<-function(param = theta,n, T,C) >> > { >> > U <- vector(mode="numeric",length=3) >> > theta[1] <- param[1] >> > theta[2] <- param[2] >> > theta[3] <- param[3] >> > r<- 17 >> > n <-30 >> > T<-c(7.048,0.743,2.404,1.374,2.233,1.52,23.531,5.182,4.502,1.362,1.15,1.86,1.692,11.659,1.631,2.212,5.451) >> > C<- >> > c(0.562,5.69,12.603,3.999,6.156,4.004,5.248,4.878,7.122,17.069,23.996,1.538,7.792) >> > U[1]<- (r/(theta[1]+theta[2]))+((n*theta[3])/theta[1])+( >> > -1*(theta[3]+1))*sum((T+theta[2])/((theta[1]+theta[2])*T+(theta[1]*theta[2])))+ >> > (-1*(theta[3]))*sum((C+theta[2])/((theta[1]+theta[2])*C+(theta[1]*theta[2]))) >> > U[2]<-(r/(theta[1]+theta[2]))+((n*theta[3])/theta[2])+ >> > (-1*(theta[3]+1))*sum((T+theta[1])/((theta[1]+theta[2])*T+(theta[1]*theta[2])))+ >> > (-1*(theta[3]))*sum((C+theta[1])/((theta[1]+theta[2])*C+(theta[1]*theta[2]))) >> > U[3]<-(r/theta[3])+(n*log(theta[1]*theta[2]))+ >> > (-1)*sum(log((T*(theta[1]+theta[2]))+(theta[1]*theta[2])))+(-1)*sum(log((C*(theta[1]+theta[2]))+(theta[1]*theta[2]))) >> > return(U) >> > } >> > hesslik<-function(param=theta,n,T,C) >> > { >> > theta[1] <- param[1] >> > theta[2] <- param[2] >> > theta[3] <- param[3] >> > r<- 17 >> > n <-30 >> > T<-c(7.048,0.743,2.404,1.374,2.233,1.52,23.531,5.182,4.502,1.362,1.15,1.86,1.692,11.659,1.631,2.212,5.451) >> > C<- >> > c(0.562,5.69,12.603,3.999,6.156,4.004,5.248,4.878,7.122,17.069,23.996,1.538,7.792) >> > G<- matrix(nrow=3,ncol=3) >> > G[1,1]<-((-1*r)/((theta[1]+theta[2])^2))+((-1*n*theta[3])/(theta[1])^2)+ >> > (theta[3]+1)*sum(((T+theta[2])/((theta[1]+theta[2])*T+(theta[1]*theta[2])))^2)+( >> > theta[3])*sum(((C+theta[2])/((theta[1]+theta[2])*C+(theta[1]*theta[2])))^2) >> > G[1,2]<-((-1*r)/((theta[1]+theta[2])^2))+ >> > (theta[3]+1)*sum(((T)/((theta[1]+theta[2])*T+(theta[1]*theta[2])))^2)+ >> > (theta[3])*sum(((C)/((theta[1]+theta[2])*C+(theta[1]*theta[2])))^2) >> > G[2,1]<-G[1,2] >> > G[1,3]<-(n/theta[1])+(-1)*sum( >> > (T+theta[2])/((theta[1]+theta[2])*T+(theta[1]*theta[2])))+(-1)*sum((C+theta[2])/((theta[1]+theta[2])*C+(theta[1]*theta[2]))) >> > G[3,1]<-G[1,3] >> > G[2,2]<-((-1*r)/((theta[1]+theta[2])^2))+((-1*n*theta[3])/(theta[2])^2)+ >> > (theta[3]+1)*sum(((T+theta[1])/((theta[1]+theta[2])*T+(theta[1]*theta[2])))^2)+( >> > theta[3])*sum(((C+theta[1])/((theta[1]+theta[2])*C+(theta[1]*theta[2])))^2) >> > G[2,3]<-(n/theta[2])+(-1)*sum((T+theta[1])/((theta[1]+theta[2])*T+(theta[1]*theta[2])))+(-1)*sum((C+theta[1])/((theta[1]+theta[2])*C+(theta[1]*theta[2]))) >> > G[3,2]<-G[2,3] >> > G[3,3]<-((-1*r)/(theta[3])^2) >> > return(G) >> > } >> > >> > and using numeric gradient and hessian matrix: >> > >> > a <- maxLik(loglik, start=c(40,50,2)) >> > Warning messages: >> > 1: In log(theta[3]) : NaNs produced >> > 2: In log(theta[1] + theta[2]) : NaNs produced >> > 3: In log(theta[1]) : NaNs produced >> > 4: In log((T * (theta[1] + theta[2])) + (theta[1] * theta[2])) : NaNs >> > produced >> > 5: In log((C * (theta[1] + theta[2])) + (theta[1] * theta[2])) : NaNs >> > produced >> > 6: In log(theta[3]) : NaNs produced >> > 7: In log(theta[1] + theta[2]) : NaNs produced >> > and so on….. >> > >> > I don't know why I get these 50 warnings although: >> > 1- The inputs of the log() function are strictly positive. >> > 2- When I evaluated the log-likelihood fuction at the very begining it gave >> > me a number(which is -56.66) and not (NAN). >> > >> > I've also tried to: >> > 1- Reparamtrize my model using lamda(i)= log(theta(i)), for i=1,2,3, so >> > that it may solve the problem, but it didn't. >> > 2- I've used the comparederivitive() function, and the analytic and numeric >> > gradients were so close. >> > >> > Any help please? >> > Maram Salem >> > >> > [[alternative HTML version deleted]] >> > >> > ______________________________________________ >> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> > 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. >> >> >> >> -- >> Arne Henningsen >> http://www.arne-henningsen.name > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
