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Re: POLY_FIT gives wrong answer !
- Subject: Re: POLY_FIT gives wrong answer !
- From: davidf(at)dfanning.com (David Fanning)
- Date: Thu, 11 May 2000 10:34:55 -0600
- Newsgroups: comp.lang.idl-pvwave
- Organization: Fanning Software Consulting
- References: <3917F8C2.A6DB4535@oma.be> <MPG.firstname.lastname@example.org> <39194749.F2D97BCF@oma.be> <MPG.email@example.com> <391A5DDF.1C7E9EF@oma.be> <MPG.firstname.lastname@example.org>
- Xref: news.doit.wisc.edu comp.lang.idl-pvwave:19552
The Powers That Be at Goddard Space Flight Center prefer their
employees have no opinions about anything. Hence, they are
refusing to publish any news articles sent from the place.
(I'm ready to file a Freedom of Information Act request to
get Stein Vidar back on-line.) Bill Thompson smuggled this
article out in his lunch box and asked if I would post it
for him. Guess we will have to re-open discussion. :-(
Subject: Re: POLY_FIT gives wrong answer !
Henk Schets <Henk.Schets@oma.be> writes:
>Maybe all of you already know this, but I didn't and I could not find
>any documentation about it.
>The problem : I have two long arrays (x- and y-values) and I made
>subarrays out of them by using an array as subscript, like
> x = fltarray(l)
> y = fltarray(l)
> indexarr = longarray(l)
>(you know, the way to sort different arrays together)
>However, when I do something like out =
>poly_fit(x[indexarr],y[indexarr]), the outcome is simply wrong ! I know
>this because other programs gave another solution (all the same).
>The only way to do it right is by making other arrays like x2 and y2 and
>doing a poly_fit on it.
>Is this a known issue ?
At first I thought that David was right, and that there was a subtle error in
the calling program. However, some experimentation leads me to believe that
the answer has to do with round-off error.
To test what was going on, I generated a random series of X points between 0
and 1, and a corresponding Y array based on a polynomial, plus some noise.
IDL> x = randomu(seed,1000)
IDL> y = 3+5*x-7*x^2+randomn(seed,1000)
Applying POLY_FIT to these data gave reasonable results.
IDL> param1 = poly_fit(x,y,2,yfit1)
I then sorted the input arrays into increasing X, and reapplied POLY_FIT,
expecting to get the same answer.
IDL> param2 = poly_fit(x(s),y(s),2,yfit2)
To my surprise, the parameters from the two runs of POLY_FIT are very close,
but not exactly the same, even though the input arrays are exactly the same,
just ordered differently. My only conclusion is that the difference must be
due to round-off error, which affects the data differently depending on the
Perhaps your case is a more extreme example of round-off error affecting the
calibration. I've definitely had problems sometimes with completely bogus
results from POLY_FIT caused by round-off error. In my case this has always
occurred when the range in X from minimum to maximum is small compared to the
values of X. In such cases, I've always found it useful to redefine the
problem to fit a polynomial to X-X0 instead of X, where X0 is comparable to
average value of X. For example, if my values of X ranged from 13479.25 to
13480.16, subtracting a typical value of X0=13480 gives me a new X' which
ranges from -0.75 to +0.16. This reduces the effect of round-off errors