Hi Spencer,
you were the only one to reply. Yes I am aware of the intrinsic /
parameter effects distinction and the advantages of LR tests and
profiling over Wald tests based on the local curvature of the
loglikelihood surface at the larger of two models being compared. My
situation is that I am comparing two nested models both of which have
uncomfortably many parameters for the amount of data available. I am
able to fit the smaller of the two models but not the larger. In this
situation neither the the Wald nor the LR test is available to me but
the score test (a.k.a. the Lagrange Multiplier test) is available to me
because it is based on the loglikelihood gradient at the smaller model.
I have been able to carry out the test by extracting
X <- smaller.nls$m$gradient()
and obtaining the extra columns of X for the parameters in larger but
not in smaller by numerical differentiation. It seems that there should
be some way of obtaining the extra columns without recourse to numerical
differentiation, though.
Cheers, Murray Jorgensen
Spencer Graves wrote:> 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
>
> Murray Jorgensen wrote:
>> 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
--
Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz Fax 7 838 4155
Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 1395 862
