On Thu, 27 Mar 2008, Peter Dalgaard wrote:
glenn andrews wrote:
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?
In a word: no. Ill-conditioning essentially means that there are one or
more directions in parameter space along which estimation is unstable.
Along such directions you get a large SE, but also a large variability
of the estimate, resulting in t values at least in the usual "-2 to +2"
range. The large variation may swamp a true effect along said direction,
though.
That seems to be about ill-conditioning in the *correlation* matrix. As I
pointed out before, the condiition number of the *covariance* matrix
(which was the original question) is scale-dependent -- uncorrelated
parameter estimators can have an arbitrarily high condition number of
their covariance matrix (it is the ratio of the largest to the smallest
variance).
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.
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Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
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