Re: error on parameters

Hello,

This is typically the sort of results that I get:

#A => 1.17818198 (   1.1)
#B => 0.327126963 (  0.31)
#C => 0.283866159 (  0.21)

#T0 => 7373.27411 (    43)
#A => 1.17818028 (  0.17)
#B => 0.327132837 ( 0.048)
#C => 0.283861776 ( 0.032)

Both set of fitted parameters are acceptable, so the error should be

~ 1e-3 for T0
~ 1e-4 for A
~5e-5 for B and C

I used strategy = 0. I did not notice any complain about the convergence, although strategy=1 would have probably give me a wrong convergence.

The uncertainties on my data are of the order of 2e-9, so I should multiply the error by 2e-9 ! I have ~ 16000 data. In addition, I do not really understand why I get an order of magnitude better result with one fit compared to the other one.

For those set of paramters, the only way to approach something with some sense would be to multiply the obtained uncertainties by my data uncertainty and to multiply by the number of data !!!

Can I get some clarifications ?

Thank.

Hello,

   min.UserState ().Error (i)

   returns the error on the fit parameter but after fitting. Before fitting (and for fixed parameters) or when the fit failed I will be equal to the value set before fitting

   Lorenzo

On Jul 9, 2009, at 9:27 PM, Patrick Dupre wrote:

> Hello,
>
> Using FunctionMinimum
>
> To get the fitted parameters, I use:
> para [i] = min.UserState ().Value (i) ;
> and to get the error on the parameter:
> d_para [i] = min.UserState ().Error (i) ;
> but this value is always equal the the uncertainty to the paramter
> that I set before the fit.
>
> Did I not understand the documentation ?
>
> ThankA

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  Patrick DUPRÉ                      |   |
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