Re: [R] model selection with spg and AIC (or, convert list to fitted model object)

Adam, Getting the variance of MLE estimator when the true parameter is on the boundary is a very difficult problem. It is known that the standard bootstrap does not work. There are some sub-sampling approaches (Springer book: Politis, Romano, Wolff), but I am not an expert on this.

Re: [R] model selection with spg and AIC (or, convert list to fitted model object)

Dear Ravi, Thank you so much for the help. I switched to using the optimx function but I continue to use the spg method (for the most part) because I found that only spg consistently converges give different datasets. I also decided to use AIC rather that a likelihood ratio test. I have a

Re: [R] model selection with spg and AIC (or, convert list to fitted model object)

Adam Zeilinger zeil0006 at umn.edu writes: Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too).

[R] model selection with spg and AIC (or, convert list to fitted model object)

Adam, See the attached R code that solves your problem and beyond. One important issue is that you are enforcing constraints only indirectly. You need to make sure that P1, P2, and P3 (which are functions of original parameters and time) are all between 0 and 1. It is not enough to impose

[R] model selection with spg and AIC (or, convert list to fitted model object)

Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a