[Re: Significance of confidence intervals in the Non-Linear Least Squares Program.]

Thanks for the response. I was not very clear in my original request.

What I am asking is if in a non-linear estimation problem using nls(), 
as the condition number of the Hessian matrix becomes larger, will the 
t-values of one or more of the parameters being estimated in general 
become smaller in absolute value -- that is, are low t-values a  
sign of an ill-conditioned Hessian?
  

Typical nls() ouput:

Formula: y ~ (a + b * log(c * x1^d + (1 - c) * x2^d))

Parameters:
 Estimate Std. Error t value Pr(>|t|) 
a  0.11918    0.07835   1.521   0.1403 
b -0.34412    0.27683  -1.243   0.2249 
c  0.33757    0.13480   2.504   0.0189 *
d -2.94165    2.25287  -1.306   0.2031 

Glenn

Prof Brian Ripley wrote:

  

On Wed, 26 Mar 2008, glenn andrews wrote:

    

I am using the non-linear least squares routine in "R" -- nls.  I have a
dataset where the nls routine outputs tight confidence intervals on the
2 parameters I am solving for.
      

nls() does not ouptut confidence intervals, so what precisely did you do?
I would recommend using confint().

BTW, as in most things in R, nls() is 'a' non-linear least squares 
routine: there are others in other packages.

    

As a check on my results, I used the Python SciPy leastsq module on the
same data set and it yields the same answer as "R" for the
coefficients.  However, what was somewhat surprising was the the
condition number of the covariance matrix reported by the SciPy leastsq
program = 379.

Is it possible to have what appear to be tight confidence intervals that
are reported by nls, while in reality they mean nothing because of the
ill-conditioned covariance matrix?
      

The covariance matrix is not relevant to profile-based confidence 
intervals, and its condition number is scale-dependent whereas the 
estimation process is very much less so.

This is really off-topic here (it is about misunderstandings about 
least-squares estimation), so please take it up with your statistical 
advisor.