[afe5a1]: inst / mle_example.m  Maximize  Restore  History

Download this file

81 lines (66 with data), 3.1 kB

 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
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
## Copyright (C) 2003,2004 Michael Creel <michael.creel@uab.es>
##
## 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 MLE functions
# Generate data
n = 1000; # how many observations?
# the explanatory variables: note that they have unequal scales
x = [ones(n,1) -rand(n,1) randn(n,1)];
theta = 1:3; # true coefficients are 1,2,3
theta = theta';
lambda = exp(x*theta);
y = randp(lambda); # generate the dependent variable
####################################
# define arguments for mle_results #
####################################
# starting values
theta = zeros(3,1);
# data
data = [y, x];
# name of model to estimate
model = "poisson";
modelargs = {0}; # if this is zero the function gives analytic score, otherwise not
# parameter names
names = str2mat("beta1", "beta2", "beta3");
title = "Poisson MLE trial"; # title for the run
# controls for bfgsmin: 30 iterations is not always enough for convergence
control = {50,0,1,1};
# This displays the results
printf("\n\nanalytic score, unscaled data\n");
[theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, title, 0, control);
# This just calculates the results, no screen display
printf("\n\nanalytic score, unscaled data, no screen display\n");
theta = zeros(3,1);
[theta, obj_value, convergence] = mle_estimate(theta, data, model, modelargs, control);
printf("obj_value = %f, to confirm it worked\n", obj_value);
# This show how to scale data during estimation, but get results
# for data in original units (recommended to avoid conditioning problems)
# This usually converges faster, depending upon the data
printf("\n\nanalytic score, scaled data\n");
[scaled_x, unscale] = scale_data(x);
data = [y, scaled_x];
theta = zeros(3,1);
[theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, title, unscale, control);
# Example using numeric score
printf("\n\nnumeric score, scaled data\n");
theta = zeros(3,1);
modelargs = {1}; # set the switch for no score
[theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, title, unscale, control);
# Example doing estimation in parallel on a cluster (requires MPITB)
# uncomment the following if you have MPITB installed
# theta = zeros(3,1);
# nslaves = 1;
# title = "MLE estimation done in parallel";
# [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, title, unscale, control, nslaves);

Get latest updates about Open Source Projects, Conferences and News.

Sign up for the SourceForge newsletter:





No, thanks