[R] How to obtain final gradient estimation from optim

[Ott Toomet]

Hi,

 | From: Stéphane Luchini <luchini at ehess.cnrs-mrs.fr>
 | 
 | > No, and optim does not even necessarily calculate a gradient.
 | > But if it does, it is supposed to be zero at a maximum....
 | 
 | It is zero at the theoretical level, it is not zero at a numerical level and 
 | it can be used to compute the Outer Product of the Gradient as an estimator 
 | of the information matrix instead of the inverse hessian. For long 
 | computation, it enables one to get an estimation of standard deviations 
 | without computing the hessian matrix which can take a long time. 

you can calculate information matrix _only_ if your gradient is in a
matrix form (as I understand this is what you call for the matrix of
the contributions).  I.e. your gradient should have one (vector)
component for each observation (or each individual).  I am not sure if
optim() can handle such (documentation says the function should return
a scalar result...).  In that case you may use BHHH method as

  hess <- function(theta, ...) {
    g <- gradient(theta, ...)
    -t(g) %*% g
  }

You may take a look at my package econ at
www.obs.ee/~siim/econ_0.0-4.tar.gz which includes BHHH method and can
return the gradient too (however, not in matrix form).  I should warn
you that it is not well documented nor tested.  If you are interested
in Newton-Raphson method, you may look at nlm() too.

 | The matrix of the contribution to the gradient with a typical element $G_{ti}$ 
 | defined as follows:
 | 
 | $$G_{ti}(y,\theta) = \partial \ell_t(u,\theta) / \partial \theta_i$$


you mean

$$G_{ti}(y,\theta) = \partial \ell_i(u,\theta) / \partial \theta_t$$

where i means observations (individuals) and t parameters?  Or am I
misunderstanding something?

Perhaps it helps

Ott