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Lots of minor fixes: mostly configuration issues.

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Change to use std::tuple when available.
Add two new distributions: inverse gamma and inverse chi squared.


[SVN r65749]
This commit is contained in:
John Maddock
2010-10-04 15:17:21 +00:00
parent 94f09f4ea2
commit 0bc7181e61
406 changed files with 8464 additions and 3527 deletions
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22
example/chi_square_std_dev_test.cpp
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@@ -1,5 +1,5 @@
// Copyright John Maddock 2006, 2007
// Copyright Paul A. Bristow 2007
// Copyright Paul A. Bristow 2010
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0.
@@ -12,8 +12,8 @@ using std::left; using std::fixed; using std::right; using std::scientific;
#include <iomanip>
using std::setw;
using std::setprecision;
#include <boost/math/distributions/chi_squared.hpp>
#include <boost/math/distributions/chi_squared.hpp>
void confidence_limits_on_std_deviation(
double Sd, // Sample Standard Deviation
@@ -31,7 +31,7 @@ void confidence_limits_on_std_deviation(
// contains the true standard deviation or it does not.
// See http://www.itl.nist.gov/div898/handbook/eda/section3/eda358.htm
// using namespace boost::math;
// using namespace boost::math; // potential name ambiguity with std <random>
using boost::math::chi_squared;
using boost::math::quantile;
using boost::math::complement;
@@ -76,10 +76,10 @@ void confidence_limits_on_std_deviation(
void confidence_limits_on_std_deviation_alpha(
double Sd, // Sample Standard Deviation
double alpha // confidence
)
double alpha // confidence
)
{ // Calculate confidence intervals for the standard deviation.
// for the alpha parameter, for a range number of observations,
// for the alpha parameter, for a range number of observations,
// from a mere 2 up to a million.
// O. L. Davies, Statistical Methods in Research and Production, ISBN 0 05 002437 X,
// 4.33 Page 68, Table H, pp 452 459.
@@ -111,7 +111,7 @@ void confidence_limits_on_std_deviation_alpha(
unsigned int N = obs[i]; // Observations
// Start by declaring the distribution with the appropriate :
chi_squared dist(N - 1);
// Now print out the data for the table row.
cout << fixed << setprecision(3) << setw(10) << right << N;
// Calculate limits: (alpha /2 because it is a two-sided (upper and lower limit) test.
@@ -244,17 +244,17 @@ void chi_squared_sample_sized(
{
// Confidence value:
cout << fixed << setprecision(3) << setw(10) << right << 100 * (1-alpha[i]);
// calculate df for a lower single-sided test:
// Calculate df for a lower single-sided test:
double df = chi_squared::find_degrees_of_freedom(
-diff, alpha[i], alpha[i], variance);
// convert to sample size:
// Convert to integral sample size (df is a floating point value in this implementation):
double size = ceil(df) + 1;
// Print size:
cout << fixed << setprecision(0) << setw(16) << right << size;
// calculate df for an upper single-sided test:
// Calculate df for an upper single-sided test:
df = chi_squared::find_degrees_of_freedom(
diff, alpha[i], alpha[i], variance);
// convert to sample size:
// Convert to integral sample size:
size = ceil(df) + 1;
// Print size:
cout << fixed << setprecision(0) << setw(16) << right << size << endl;