Hi R-community, I wonder if anyone has dealt with this problem? I've written a negative log-likelihood function of 4 parameters, and I want to minimize it. It doesn't have derivative information (it actually requires running an external program). I can detect a gradient in it, e.g.:
> toy.likelihood.4.2(c(80.5, 43.0, 0.385, 6.5)) [1] 24664.62 > toy.likelihood.4.2(c(79.5, 43.0, 0.385, 6.5)) [1] 24657.32 > toy.likelihood.4.2(c(79.5, 43.0, 0.375, 6.5)) [1] 24669.77 but nlm can't detect a gradient in that region: > unweighted.mle.1 <- nlm(toy.likelihood.4.2, + c(80.5, 43.0, 0.385 6.5), + hessian=T, print.level=2) iteration = 0 Parameter: [1] 80.500 43.000 0.385 6.500 Function Value [1] 24664.62 Gradient: [1] 0 0 0 0 Relative gradient close to zero. Current iterate is probably solution. Can anyone suggest a remedy? Thanks much, Andrew ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html