From: john_at_[hidden]
Date: 2007-08-05 13:57:37
Author: johnmaddock
Date: 2007-08-05 13:57:35 EDT (Sun, 05 Aug 2007)
New Revision: 38457
URL: http://svn.boost.org/trac/boost/changeset/38457
Log:
Renamed remotely
Added:
sandbox/math_toolkit/libs/math/example/neg_binomial_confidence_limits.cpp
- copied unchanged from r38456, /sandbox/math_toolkit/libs/math/example/Neg_binomial_confidence_limits.cpp
Removed:
sandbox/math_toolkit/libs/math/example/Neg_binomial_confidence_limits.cpp
Deleted: sandbox/math_toolkit/libs/math/example/Neg_binomial_confidence_limits.cpp
==============================================================================
--- sandbox/math_toolkit/libs/math/example/Neg_binomial_confidence_limits.cpp 2007-08-05 13:57:35 EDT (Sun, 05 Aug 2007)
+++ (empty file)
@@ -1,179 +0,0 @@
-// neg_binomial_confidence_limits.cpp
-
-// Copyright John Maddock 2006
-// Copyright Paul A. Bristow 2007
-// Use, modification and distribution are subject to the
-// Boost Software License, Version 1.0.
-// (See accompanying file LICENSE_1_0.txt
-// or copy at http://www.boost.org/LICENSE_1_0.txt)
-
-// Caution: this file contains quickbook markup as well as code
-// and comments, don't change any of the special comment markups!
-
-//[neg_binomial_confidence_limits
-
-/*`
-
-First we need some includes to access the negative binomial distribution
-(and some basic std output of course).
-
-*/
-
-#include <boost/math/distributions/negative_binomial.hpp>
-using boost::math::negative_binomial;
-
-#include <iostream>
-using std::cout; using std::endl;
-#include <iomanip>
-using std::setprecision;
-using std::setw; using std::left; using std::fixed; using std::right;
-
-/*`
-First define a table of significance levels: these are the
-probabilities that the true occurrence frequency lies outside the calculated
-interval:
-*/
-
- double alpha[] = { 0.5, 0.25, 0.1, 0.05, 0.01, 0.001, 0.0001, 0.00001 };
-
-/*`
-
-Confidence value as % is (1 - alpha) * 100, so alpha 0.05 == 95% confidence
-that the true occurence frequency lies *inside* the calculated interval.
-
-We need a function to calculate and print confidence limits
-for an observed frequency of occurrence
-that follows a negative binomial distribution.
-
-*/
-
-void confidence_limits_on_frequency(unsigned trials, unsigned successes)
-{
- // trials = Total number of trials.
- // successes = Total number of observed successes.
- // failures = trials - successes.
- // success_fraction = successes /trials.
- // Print out general info:
- cout <<
- "______________________________________________\n"
- "2-Sided Confidence Limits For Success Fraction\n"
- "______________________________________________\n\n";
- cout << setprecision(7);
- cout << setw(40) << left << "Number of trials" << " = " << trials << "\n";
- cout << setw(40) << left << "Number of successes" << " = " << successes << "\n";
- cout << setw(40) << left << "Number of failures" << " = " << trials - successes << "\n";
- cout << setw(40) << left << "Observed frequency of occurrence" << " = " << double(successes) / trials << "\n";
-
- // Print table header:
- cout << "\n\n"
- "___________________________________________\n"
- "Confidence Lower Upper\n"
- " Value (%) Limit Limit\n"
- "___________________________________________\n";
-
-
-/*`
-And now for the important part - the bounds themselves.
-For each value of /alpha/, we call `find_lower_bound_on_p` and
-`find_upper_bound_on_p` to obtain lower and upper bounds respectively.
-Note that since we are calculating a two-sided interval,
-we must divide the value of alpha in two. Had we been calculating a
-single-sided interval, for example: ['"Calculate a lower bound so that we are P%
-sure that the true occurrence frequency is greater than some value"]
-then we would *not* have divided by two.
-
-*/
-
- // Now print out the upper and lower limits for the alpha table values.
- for(unsigned i = 0; i < sizeof(alpha)/sizeof(alpha[0]); ++i)
- {
- // Confidence value:
- cout << fixed << setprecision(3) << setw(10) << right << 100 * (1-alpha[i]);
- // Calculate bounds:
- double lower = negative_binomial::find_lower_bound_on_p(trials, successes, alpha[i]/2);
- double upper = negative_binomial::find_upper_bound_on_p(trials, successes, alpha[i]/2);
- // Print limits:
- cout << fixed << setprecision(5) << setw(15) << right << lower;
- cout << fixed << setprecision(5) << setw(15) << right << upper << endl;
- }
- cout << endl;
-} // void confidence_limits_on_frequency(unsigned trials, unsigned successes)
-
-/*`
-
-And then call confidence_limits_on_frequency with increasing numbers of trials,
-but always the same success fraction 0.1, or 1 in 10.
-
-*/
-
-int main()
-{
- confidence_limits_on_frequency(20, 2); // 20 trials, 2 successes, 2 in 20, = 1 in 10 = 0.1 success fraction.
- confidence_limits_on_frequency(200, 20); // More trials, but same 0.1 success fraction.
- confidence_limits_on_frequency(2000, 200); // Many more trials, but same 0.1 success fraction.
-
- return 0;
-} // int main()
-
-//] [/negative_binomial_confidence_limits_eg end of Quickbook in C++ markup]
-
-/*
-
-______________________________________________
-2-Sided Confidence Limits For Success Fraction
-______________________________________________
-Number of trials = 20
-Number of successes = 2
-Number of failures = 18
-Observed frequency of occurrence = 0.1
-___________________________________________
-Confidence Lower Upper
- Value (%) Limit Limit
-___________________________________________
- 50.000 0.04812 0.13554
- 75.000 0.03078 0.17727
- 90.000 0.01807 0.22637
- 95.000 0.01235 0.26028
- 99.000 0.00530 0.33111
- 99.900 0.00164 0.41802
- 99.990 0.00051 0.49202
- 99.999 0.00016 0.55574
-______________________________________________
-2-Sided Confidence Limits For Success Fraction
-______________________________________________
-Number of trials = 200
-Number of successes = 20
-Number of failures = 180
-Observed frequency of occurrence = 0.1000000
-___________________________________________
-Confidence Lower Upper
- Value (%) Limit Limit
-___________________________________________
- 50.000 0.08462 0.11350
- 75.000 0.07580 0.12469
- 90.000 0.06726 0.13695
- 95.000 0.06216 0.14508
- 99.000 0.05293 0.16170
- 99.900 0.04343 0.18212
- 99.990 0.03641 0.20017
- 99.999 0.03095 0.21664
-______________________________________________
-2-Sided Confidence Limits For Success Fraction
-______________________________________________
-Number of trials = 2000
-Number of successes = 200
-Number of failures = 1800
-Observed frequency of occurrence = 0.1000000
-___________________________________________
-Confidence Lower Upper
- Value (%) Limit Limit
-___________________________________________
- 50.000 0.09536 0.10445
- 75.000 0.09228 0.10776
- 90.000 0.08916 0.11125
- 95.000 0.08720 0.11352
- 99.000 0.08344 0.11802
- 99.900 0.07921 0.12336
- 99.990 0.07577 0.12795
- 99.999 0.07282 0.13206
-*/
\ No newline at end of file