nmregr
Calculates least squares estimates using Nelder Mead's simplex method with the "symmetric bounded" loss function
Contents
Syntax
[q, info] = nmregr (func, p, varargin)
Description
Calculates least squares estimates using Nelder Mead's simplex method
Input
- func: string with name of user-defined function
f = func (p, xyw) with
p: k-vector with parameters; xyw: (n,c)-matrix; f: n-vector
[f1, f2, ...] = func (p, xyw1, xyw2, ...) with p: k-vector and
xywi: (ni,k)-matrix; fi: ni-vector with model predictions
The dependent variable in the output f; For xyw see below.- p: (k,2)-matrix with
p(:,1) initial guesses for parameter values p(:,2) binaries with yes or no iteration (optional)
- xyzi (read as xyw1, xyw2, .. ): (ni,3) matrix with
xywi(:,1) independent variable i xywi(:,2) dependent variable i xywi(:,3) weight coefficients i (optional) xywi(:,>3) data-pont specific information data (optional) The number of data matrices xyw1, xyw2, ... is optional but >0 Default for xywi(:,3) is (number of data points in the set i)^-1
Output
- q: matrix like p, but with F_sb estimates
- info: 1 if convergence has been successful; 0 otherwise
- fval: scalar with value of loss function at estimates
Remarks
Calls user-defined function 'func' Set options with nmregr_options See nrregr for Newton-Raphson method, garegr for genetic algorithm, nrregr2 for 2 independent variables, and nmvcregr for standard deviations proportional to the mean. It is usually a good idea to run nrregr on the result of nmregr.
Example of use
See mydata_regr