Hi, You did not give us any information about your likelihood function, f, nor did you provide a reproducible example. So, I cannot tell for sure whether the parameter estimates are reliable.
Ravi. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Sally Luo Sent: Wednesday, August 25, 2010 11:26 AM To: r-help@r-project.org Subject: [R] What does this warning message (from optim function) mean? Hi R users, I am trying to use the optim function to maximize a likelihood funciton, and I got the following warning messages. Could anyone explain to me what messege 31 means exactly? Is it a cause for concern? Since the value of convergence turns out to be zero, it means that the converging is successful, right? So can I assume that the parameter estimates generated thereafter are reliable MLE estimates? Thanks a lot for your help. Maomao ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > p<-optim(c(0,0,0), f, method ="BFGS", hessian =T, y=y,X=X,W=W) There were 31 warnings (use warnings() to see them) > warnings() Warning messages: 1: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 2: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 3: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 4: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 5: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 6: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 7: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 8: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 9: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 10: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 11: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 12: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 13: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 14: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 15: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 16: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 17: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 18: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 19: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 20: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 21: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 22: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 23: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 24: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 25: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 26: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 27: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 28: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 29: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 30: In log(det(I_N - pd * wd - po * wo - pw * ww)) : NaNs produced 31: In if (hessian) { ... : the condition has length > 1 and only the first element will be used > p$counts function gradient 148 17 > p$convergence [1] 0 [[alternative HTML version deleted]] ______________________________________________ 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. ______________________________________________ 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.