# Re: [R] help with the maxBHHH routine

Date: Tue, 03 May 2011 21:34:00 -0400

maxBHHH is *not* an in-built R function. It is in a distributed package called "maxLik". Always tell us which package is being used so that it is easier for us to help you.

The error message says that the gradient function is returning a 10 x 2 matrix, whereas you say that you have 1000's of observations and 821 parameters. Show us a simplified version of your problem. It is difficult to help you without seeing an example.

Ravi.

From: r-help-bounces_at_r-project.org [r-help-bounces_at_r-project.org] On Behalf Of Rohit Pandey [rohitpandey576_at_gmail.com] Sent: Tuesday, May 03, 2011 4:53 PM
To: r-help_at_r-project.org
Subject: [R] help with the maxBHHH routine

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:

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.

```--
Rohit
Mob: 91 9819926213

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Received on Thu 05 May 2011 - 06:25:11 GMT

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