Hello Heinz,
You can have a look at pages 45-49 of my slides on least Squares :
http://www.utc.fr/~mottelet/mt94/leastSquares.pdf
Page 48 you have an example where the Covariance matrix is
approximated for a fitting problem with an ode defined page 42.
S.
Quoting Heinz Nabielek :
Scilab
Scilab friends: the power of Scilab is amazing and I have used it recently for
non-linear least-squares fitting, below example from Scilab help function for
"datafit". On occasions, I have also used "leastsq".
Question: how do I derive the 1sigma standard error in the three parameters
p(1),
Le 04/04/2020 à 09:26, Federico Miyara a écrit :
Dear All,
I'm trying to understand the function datafit.
The documentation says:
datafit is used for fitting data to a model. For a given function
G(p,z), this function finds the best vector of parameters p for
approximating
Dear All,
I'm trying to understand the function datafit.
The documentation says:
datafit is used for fitting data to a model. For a given function
G(p,z), this function finds the best vector of parameters p for
approximating G(p,z_i)=0 for a set of measurement vectors z_i.
Vector
Stéphane,
Yeah, but really badly conditionned compared to the above method which
is based on orthogonal tranformations (X=Q*R factorization). With your
below method you solve a linear system with X'*X matrix which has a
condition number which is the square of the condition number of the R
