From telford@progsoc.uts.edu.au  Mon Jul 31 03:11:44 2000
From: telford@progsoc.uts.edu.au (Telford Tendys)
Date: Mon, 31 Jul 2000 12:11:44 +1000
Subject: [Rd] Using the nls package
In-Reply-To: <6rlmykbi86.fsf@franz.stat.wisc.edu>; from bates@stat.wisc.edu on Sat, Jul 29, 2000 at 06:59:53PM -0500
References: <20000728195151.A18822@ftoomsh.progsoc.uts.edu.au> <6rlmykbi86.fsf@franz.stat.wisc.edu>
Message-ID: <20000731121144.B18822@ftoomsh.progsoc.uts.edu.au>

On Sat, Jul 29, 2000 at 06:59:53PM -0500, Douglas Bates wrote:
> The convergence criterion for nls is the relative offset orthogonality
> convergence criterion as described in 
> @Article{bate:watt:1981:tech,
>   journal =      "Technometrics",
>   volume =       23,
>   pages =        "179--183",
>   keywords =     "Regression",
>   author =       "Douglas M. Bates and Donald G. Watts",
>   title =        "A Relative Offset Orthogonality Convergence Criterion
>                  for Nonlinear Least Squares",
>   year =         1981
> }

OK, I'll try to look that one up. You probably have a very good
convergence criterion for realistic sample data (which will always
have some error).

> The problem with putting in a check for an exceptionally small sum of
> squares is deciding when it is exceptionally small.  There is no
> absolute scale on the residual sum of squares.
> 
> We could perhaps detect an exact zero but even that would not catch
> all the cases where "exact" values were simulated because generally
> they will give a small but non-zero sum of squares.

Hmmm, I see your point here... the size of the residual sum of squares can 
change deastically when you add or delete measurement points, try different
models, etc so it's value only has meaning when relative to other residual
sum of squares for the same model, for the same data points.

How about testing for progress? If the nls algorithm is giving the same
numbers on two consecutive cycles then we can be sure it is not going to
get any better with more iterations so:

[1] keep a copy of the parameters from last cycle
[2] keep a copy of the sum of square residuals from last cycle
[3] if the sum of the residuals has changed at all then this cycle
    is making progress so keep cycling
[4] if the sum of residuals has not changed, check if the parameters
    have changed, if they have not changed then give up because
    next cycle will be same as this one.

Maybe [1] and [4] are not necessary and checking for change in the
sum of square residuals is enough but I am vaguely aware that it might
be possible to have the parameters change without the sum of square
residuals changing (I would have to study the algorithm very closely
to figure out whether that was possible).

Anyhow, would copying the parameters each cycle be a big overhead?

> Since such problems will not occur in practice but only in artificial
> examples, we have not bothered detecting them.

Yes, this is understandable but if most people are like me (questionable
assumption I admit) they try a few artificial examples first to get
the general idea of how these things work. I guess there is a certain
illogical assumption that if I try what seems to me to be an obviously
easy problem and it cannot handle it then something must be wrong and
trying harder problems is not going to help.

> > Is there any chance of getting this to work? Should I split up the
> > complex numbers into components and write my equations are equivalent
> > expressions in reals (ugly but I could do it if I have to)?
> 
> That's the best short-term solution.

OK, I'll go with that.

> In general, how do you want to
> define the residual sum of squares for a complex-valued response?
> Should it be the sum of the modulus of the residuals?

My thinking was that the `obvious' choice is the sum of the
SQUARES of the modulus of the residuals. Using the modulus itself
would be similar to using the absolute value of the residuals in the
non-complex case (not what we want I believe).

Another way of looking at it would be to think of extending the
residual from a scalar to a vector -- the natural extension of the
`square' operation is to square each element in the vector.
The current R system already uses this as the natural extension
to multiply and everyone seems happy with that:

> square <- function( x ) { x * x }
> square( -3 )
[1] 9
> square( -3:4 )
[1]  9  4  1  0  1  4  9 16

So once you have squared all the elements it makes sense to sum them
up, just like you do with the residuals in any case. Then you get
support for vector square residuals as being the same as the dot product
of the vector with itself (i.e. the only way to multiply a vector
with itself to get a scalar).

Keeping the logic going, a complex number is sort of like a special
case of a two element vector (yeah I know they aren't exactly the
same) so squaring the real and squaring the imaginary parts and summing
them up would seem consistent. But then Pythagoras points out that
this is the same as the square of the modulus in any case,
a full circle has been drawn.

> We could put complex-valued nonlinear least squares on the wishlist
> but I would have to warn you that there are many other items in front
> of it.

wish wish wish...

Now I think of it, there are two steps in this wish.
The first is to allow complex numbers to be allowed in the model
calculation at all and thus allow them to be residuals.

The second is to think about whether the parameters should be
allowed to be complex. I would argue that this second one is not
a good idea because it is rather easy to convert two real valued 
model parameters into a single complex number -- this ensures that
each model parameter is exactly one degree of freedom.

On the other hand, it is kind of painful to expand out complex
calculations into their real equivalents, and frustrating too when
there is a perfectly good complex calculator so close, yet so far away.

In either case, the documentation should mention that complex
numbers are not supported.

	- Tel
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[Rd] Using the nls package
Telford Tendys