I suggest that you provide some commented, minimal, self-contained, reproducible code.
Cheers Andrew On Wed, May 04, 2011 at 02:23:29AM +0530, Rohit Pandey wrote: > Hello R community, > > I have been using R's inbuilt maximum likelihood functions, for the > different methods (NR, BFGS, etc). > > I have figured out how to use all of them except the maxBHHH function. This > one is different from the others as it requires an observation level > gradient. > > I am using the following syntax: > > maxBHHH(logLik,grad=nuGradient,finalHessian="BHHH",start=prm,iterlim=2) > > where logLik is the likelihood function and returns a vector of observation > level likelihoods and nuGradient is a function that returns a matrix with > each row corresponding to a single observation and the columns corresponding > to the gradient values for each parameter (as is mentioned in the online > help). > > however, this gives me the following error: > > *Error in checkBhhhGrad(g = gr, theta = theta, analytic = (!is.null(attr(f, > : > the matrix returned by the gradient function (argument 'grad') must have > at least as many rows as the number of parameters (10), where each row must > correspond to the gradients of the log-likelihood function of an individual > (independent) observation: > currently, there are (is) 10 parameter(s) but the gradient matrix has only > 2 row(s) > * > It seems it is expecting as many rows as there are parameters. So, I changed > my likelihood function so that it would return the transpose of the earlier > matrix (hence returning a matrix with rows equaling parameters and columns, > observations). > > However, when I run the function again, I still get an error: > *Error in gr[, fixed] <- NA : (subscript) logical subscript too long* > > I have verified that my gradient function, when summed across observations > gives the same results as the in built numerical gradient (to the 11th > decimal place - after that, they differ since R's function is numerical). > > I am trying to run a very large estimation (1000's of observations and 821 > parameters) and all of the other methods are taking way too much time > (days). This method is our last hope and so, any help will be greatly > appreciated. > > -- > Thanks in advance, > Rohit > Mob: 91 9819926213 > > [[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. -- Andrew Robinson Program Manager, ACERA Department of Mathematics and Statistics Tel: +61-3-8344-6410 University of Melbourne, VIC 3010 Australia (prefer email) http://www.ms.unimelb.edu.au/~andrewpr Fax: +61-3-8344-4599 http://www.acera.unimelb.edu.au/ Forest Analytics with R (Springer, 2011) http://www.ms.unimelb.edu.au/FAwR/ Introduction to Scientific Programming and Simulation using R (CRC, 2009): http://www.ms.unimelb.edu.au/spuRs/ ______________________________________________ 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.
