nonlinear model: pseudo-design matrix

There doubtless is a way to extract the gradient information you 
desire, but have you considered profiling instead?  Are you familiar 
with the distinction between intrinsic and parameter effects curvature? 
  In brief, part of the nonlinearities involved in nonlinear least 
squares are intrinsic to the problem, and part are due to the how the 
problem is parameterized.  If you change the parameterization, you 
change the parameter effects curvature, but the intrinsic curvature 
remains unchanged.  Roughly 30 years ago, Doug Bates and Don Watts 
reanalized a few dozen published nonlinear regression fits, and found 
that in all but perhaps one or two, the parameter effects were dominant 
and the intrinsic curvature was negligible.  See Bates and Watts (1988) 
Nonlinear Regression Analysis and Its Applications (Wiley) or Seber and 
Wild (1989) Nonlinear Regression (Wiley).

	  Bottom line:

	  1.  You will always get more accurate answers from profiling than 
from the Wald "pseudodesign matrix" approach.  Moreover, often the 
differences are dramatic.

	  2.  I just did RSiteSearch("profiling with nls").  The first hit was 
"http://finzi.psych.upenn.edu/R/library/stats/html/profile.nls.html". 
If this is not satisfactory, please explain why.

	  hope this helps.
	  spencer graves

Given a nonlinear model formula and a set of values for all the
parameters defining a point in parameter space, is there a neat way to
extract the pseudodesign matrix of the model at the point? That is the
matrix of partial derivatives of the fitted values w.r.t. the parameters
evaluated at the point.

(I have figured out how to extract the gradient information from an nls 
fitted model using the nlsModel part, but I wish to implement a score 
test, so I need to be able to extract the information at points other 
than the mle.)

Thanks, Murray Jorgensen