Prof. Nash, awesome! This sounds promising.
Thank you for the explanation, Jean 2015-05-08 14:16 GMT-07:00 Prof J C Nash (U30A) <[email protected]>: > Your problem is saying (on my machine) that it cannot compute the > gradient. Since it does this numerically, my guess is that the step to > evaluate the gradient violates the bounds and we get log(-something). > > I also get > > > Warning messages: > > 1: In dnbinom(x = dummyData[, "Y"], mu = mu, size = > params[length(params)], : > > NaNs produced > > 2: In nlminb(start = sv, objective = nLL, lower = 0, upper = Inf, > control = list(trace = TRUE)) : > > NA/NaN function evaluation > > 3: In dnbinom(x = dummyData[, "Y"], mu = mu, size = > params[length(params)], : > > NaNs produced > > 4: In nlminb(start = sv, objective = nLL, lower = 0, upper = Inf, > control = list(trace = TRUE)) : > > NA/NaN function evaluation > > > > I put lower=0.01 and got convergence OK, but that may not be suitable, > since all but one of the parameters are at that bound. > > > $par > > [1] 0.01000000 0.01000000 0.01000000 0.01000000 0.01000000 0.01000000 > > [7] 0.01000000 0.01000000 0.01000000 0.01000000 0.01000000 0.01000000 > > [13] 0.09168027 > > > > $objective > > [1] 11879.51 > > > > $convergence > > [1] 0 > > > > $iterations > > [1] 8 > > > > $evaluations > > function gradient > > 13 119 > > > > $message > > [1] "relative convergence (4)" > > > As it turns out, Duncan Murdoch, Ben Bolker and I had a meeting > yesterday to discuss improvements in optimization and nonlinear least > squares and derivatives. One suggestion to be implemented is a wrapper > for objective functions to reveal when bounds are violated. It will, > however, take a little time for me to get that organized. > > FYI, without the reproducible example, you would not have received this > attempt to explain things. Thanks. > > JN > > > > > A follow-up to my yesterday's email. > > > > I was able to make a reproducible example. All you will have to do is > > load the .RData file that you can download here: > > > https://drive.google.com/file/d/0B0DKwRjF11x4dG1uRWhwb1pfQ2s/view?usp=sharing > > > > and run this line of code: > > > > nlminb(start=sv, objective = nLL, lower = 0, upper = Inf, > > control=list(trace=TRUE)) > > > > which output the following: > > > > 0: 12523.401: 0.0328502 0.0744493 0.00205298 0.0248628 0.0881807 > > 0.0148887 0.0244485 0.0385922 0.0714495 0.0161784 0.0617551 0.0244901 > > 0.0784038 > > 1: 12421.888: 0.0282245 0.0697934 0.00000 0.0199076 0.0833634 > > 0.0101135 0.0189494 0.0336236 0.0712130 0.0160687 0.0616015 0.0244689 > > 0.0660129 > > 2: 12050.535: 0.00371847 0.0451786 0.00000 0.00000 0.0575667 > > 0.00000 0.00000 0.00697067 0.0697205 0.0156250 0.0608550 0.0243431 > > 0.0994355 > > 3: 12037.682: 0.00303460 0.0445012 0.00000 0.00000 0.0568530 > > 0.00000 0.00000 0.00636016 0.0696959 0.0156250 0.0608550 0.0243419 > > 0.0988824 > > 4: 12012.684: 0.00164710 0.0431313 0.00000 0.00000 0.0554032 > > 0.00000 0.00000 0.00515500 0.0696451 0.0156250 0.0608550 0.0243395 > > 0.0978328 > > 5: 12003.017: 0.00107848 0.0425739 0.00000 0.00000 0.0548073 > > 0.00000 0.00000 0.00469592 0.0696233 0.0156250 0.0608550 0.0243386 > > 0.0974616 > > 6: 11984.372: 0.00000 0.0414397 0.00000 0.00000 0.0535899 > > 0.00000 0.00000 0.00378996 0.0695782 0.0156250 0.0608550 0.0243370 > > 0.0967449 > > 7: 11978.154: 0.00000 0.0409106 0.00000 0.00000 0.0530158 > > 0.00000 0.00000 0.00340746 0.0695560 0.0156250 0.0608550 0.0243363 > > 0.0964537 > > 8: -0.0000000: 0.00000 nan 0.00000 0.00000 nan > > 0.00000 0.00000 nan nan nan nan nan nan > > > > Regards, > > > > Jean > > > > 2015-05-06 17:43 GMT-07:00 Jean Marchal <[email protected]>: > >> Dear list, > >> > >> I am doing some maximum likelihood estimation using nlminb() with > >> box-constraints to ensure that all parameters are positive. However, > >> nlminb() is behaving strangely and seems to supply NaN as parameters > >> to my objective function (confirmed using browser()) and output the > >> following: > >> > >> $par > >> [1] NaN NaN NaN 0 NaN 0 NaN NaN NaN NaN NaN NaN NaN > >> > >> $objective > >> [1] 0 > >> > >> $convergence > >> [1] 1 > >> > >> $iterations > >> [1] 19 > >> > >> $evaluations > >> function gradient > >> 87 542 > >> > >> $message > >> [1] "gr cannot be computed at initial par (65)" > >> > > [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
