[R] help with the maxBHHH routine

[Andrew Robinson]

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]]
> 
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> 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/