User`s guide

3 Fitting Data
3-24
Fitting Method and Algorithm
• Method — The fitting method.
The method is automatically selected based on the library or custom model
you use. For linear models, the method is
LinearLeastSquares. For
nonlinear models, the method is
NonlinearLeastSquares.
•
Robust — Specify whether to use the robust least squares fitting method.
The values are
-
Off — Do not use robust fitting (default).
-
On — Fit with default robust method (bisquare weights).
-
LAR — Fit by minimizing the least absolute residuals (LAR).
-
Bisquare — Fit by minimizing the summed square of the residuals, and
downweight outliers using bisquare weights. In most cases, this is the best
choice for robust fitting.
•
Algorithm — Algorithm used for the fitting procedure:
-
Trust-Region — This is the default algorithm and must be used if you
specify coefficient constraints.
-
Levenberg-Marquardt — If the trust-region algorithm does not produce
a reasonable fit, and you do not have coefficient constraints, you should try
the Levenberg-Marquardt algorithm.
-
Gauss-Newton — This algorithm is included for pedagogical reasons and
should be the last choice for most models and data sets.
Finite Differencing Parameters
• DiffMinChange — Minimum change in coefficients for finite difference
Jacobians. The default value is 10
-8
.
•
DiffMaxChange — Maximum change in coefficients for finite difference
Jacobians. The default value is 0.1.
Fit Convergence Criteria
• MaxFunEvals — Maximum number of function (model) evaluations
allowed. The default value is 600.
•
MaxIter — Maximum number of fit iterations allowed. The default value is
400.