## [afe5a1]: inst / nls_example.m Maximize Restore History

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60``` ```## Copyright (C) 2006 Michael Creel ## ## This program is free software; you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation; either version 2 of the License, or ## (at your option) any later version. ## ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with this program; if not, write to the Free Software ## Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ## Example to show how to use NLS # Generate data n = 100; # how many observations? # the explanatory variables: note that they have unequal scales x = [ones(n,1) rand(n,2)]; theta = 1:3; # true coefficients are 1,2,3 theta = theta'; lambda = exp(x*theta); y = randp(lambda); # generate the dependent variable # example objective function for nls function [obj_contrib, score] = nls_example_obj(theta, data, otherargs) y = data(:,1); x = data(:,2:columns(data)); lambda = exp(x*theta); errors = y - lambda; obj_contrib = errors .* errors; score = "na"; endfunction #################################### # define arguments for nls_estimate # #################################### # starting values theta = zeros(3,1); # data data = [y, x]; # name of model to estimate model = "nls_example_obj"; modelargs = {}; # none required for this obj fn. # controls for bfgsmin control = {50,1,1,1}; printf("\n\NLS estimation example\n"); [theta, obj_value, convergence] = nls_estimate(theta, data, model, modelargs, control); ```