# [SciPy-User] Question about errors (uncertainties) in non-linear least squares fitting

Paweł Kwaśniewski pawel.kw@gmail....
Tue Aug 7 16:31:37 CDT 2012

```Dear Jonathan,

Thank you for the quick answer and pointing me to the updated code and the
literature. I'll certainly have a look into it. Just for the record, I
wanted to use constrained fitting because the unconstrained fit sometimes
gave me unphysical parameters.

It's interesting what you wrote about how the constraint influences the
parameter error estimate. Just to give it a shot I tried an unconstrained
fit on one data set. It did converge, but still - I got enormous chi^2
(reduced, and yes - I'm using the experimental errors to calculate it) with
ridiculously small parameter errors... I guess I need to understand better
what's going on.

By the way, the constrained fitting function is great, but I'd like to add
another feature - the possibility to fix a parameter. This would largely
facilitate testing of the fitting function, or can even be useful in real
applications, where one of the parameters is known from a previous fit of a
slightly different model or a different measurement or whatever... Anyway -
maybe you have any ideas how to implement that? I already tried setting the
upper and lower bound to the same value - it doesn't work. The method you
use to apply the bounds throws division by zero error. **

Cheers,

Paweł

2012/8/7 Jonathan Helmus <jjhelmus@gmail.com>

>  Pawel,
>
>     First off you may want to use a more up to date version of
> leastsqbound which can be found at
> https://github.com/jjhelmus/leastsqbound-scipy
>
>     Second, when you perform a constrained optimization using internal
> parameters like leastsqbound does,
> if one of more of the parameters is close to a bound, the values in the
> covariance matrix can take on meaningless values.  Section 1.3 of the The
> Minuit User's Guide [1] gives a good overview of this, especially look at
> the discussion on page 5.  For best results an unconstrained optimization
> should be performed, often times you can rewrite your model in such a way
> that the constraints are automatically imposed (this is what is done
> internally in leastsqbound, but transforming back to the original model can
> introduce large errors if a parameter is close to the bounds).
>
>     Third, since you have measurement uncertainties make sure you include
> them in the chi^2 calculation.  I find the discussion by P.H. Richter [2]
> to be quite good.
>
> Cheers,
>
>     - Jonathan Helmus
>
>
>
>
> [1] http://seal.cern.ch/documents/minuit/mnusersguide.pdf
> [2] Estimating Errors in Least-Squares Fitting, P.H. Richter TDA Progress
> Report 42-122
>     http://tmo.jpl.nasa.gov/progress_report/42-122/122E.pdf
>
>
> On 08/07/2012 09:16 AM, Paweł Kwaśniewski wrote:
>
> Hi,
>
> I'm fitting some data using a wrapper around the scipy.optimize.leastsq
> method which can be found under
> http://code.google.com/p/nmrglue/source/browse/trunk/nmrglue/analysis/leastsqbound.pyBasically it allows for putting bounds on the fitted parameters, which is
> very important for me.
>
> I'm using the covariance matrix, returned by leastsq() function to
> estimate the errors of the fitted parameters. The fitting is done using
> real measurement uncertainties (which are ridiculously small, by the way),
> so I would expect the resulting parameter error to be reasonable. What
> don't understand, is that I'm getting extremely small errors on the fitted
> parameters (I calculate the errors as perr = sqrt(diag(fitres[1])), where
> fitres[1] is the covariance matrix returned by leastsq() function). For
> example, a parameter which has a fitted value of ~100 gets an error of
> ~1e-6. At the same time, when I calculate the reduced chi squared of the
> fit I'm getting an extremely large number (of the order of 1e8). I can
> understand the large chi^2 value - the data variance is extremely small and
> the model curve is not perfect, so even slight deviations of the fitted
> model from the data will blow up chi^2 into space. But how can the fitted
> parameter variance be so small, while at the same time the fit is garbage
> according to chi^2?
>
> I guess this requires a better understanding of how the covariance matrix
> is calculated. Some suggestions anyone?
>
> Cheers,
>
> Paweł
>
>
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