[Boost-commit] svn:boost r38457 - sandbox/math_toolkit/libs/math/example

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