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Go to the source code of this file.
Functions | |
| void | BinLogLikelihoodRatio (long, long, double, double *, double *) |
| void | PoissionLogLikelihoodRatio (double, double, double, double, double *, double *) |
| void BinLogLikelihoodRatio | ( | long | , |
| long | , | ||
| double | , | ||
| double * | , | ||
| double * | |||
| ) |
Definition at line 7 of file QStatisticalTests.cc.
References MultiGaussianStateTransform::N, n, and mathSSE::sqrt().
{
/*--------------------------------------------------------------------------+
| Description: Log-likelihood Ratio for Binomial PDF |
+--------------------------------------------------------------------------+
| Input to this function |
+--------------------------------------------------------------------------+
|int Nentries : The number of attempts |
|int Nfailures : The number of failures |
|double epsilon_max, : maximum allowed failure rate fraction |
|double* S_fail_obs : uninitialised Significance of failure |
|double* S_pass_obs : uninitialised Significance of Success |
+--------------------------------------------------------------------------+
| Return values for this function |
+--------------------------------------------------------------------------+
|double* S_fail_obs : the observed Significance of failure |
|double* S_pass_obs : the observed Significance of Success |
+--------------------------------------------------------------------------+
| Author: Richard Cavanaugh, University of Florida |
| email: Richard.Cavanaugh@cern.ch |
| Creation Date: 11.July.2005 |
| Last Modified: 17.Jan.2006 |
| Comments: |
+--------------------------------------------------------------------------*/
long N = Nentries, n = Nfailures;
if( n == 0 ) n = 1; //protect against no failures: approx. 0 by 1
if( n == N ) n -= 1; //protect against all failures: approx. n by (n - 1)
double epsilon_meas = (double) n / (double) N;
double LogQ =
((double) n ) * ( Log(epsilon_meas) - Log(epsilon_max) ) +
((double) (N - n)) * ( Log(1.0 - epsilon_meas) - Log(1.0 - epsilon_max) );
//x-check: var of binomial = epsilon_max * (1 - epsilon_max) / N
if( Nentries <= 1 ) //guard against insufficient entries
{
*S_fail_obs = 0.0;
*S_pass_obs = 0.0;
}
else if( Nfailures == 0 && ( epsilon_max <= 1.0 / (double) Nentries ) )
{
*S_fail_obs = 0.0;
*S_pass_obs = 0.0;
}
else if( Nfailures == 0 )
{
*S_fail_obs = 0.0;
*S_pass_obs = sqrt( 2.0 * LogQ );
}
else if( Nfailures == Nentries )
{
*S_fail_obs = sqrt( 2.0 * LogQ );
*S_pass_obs = 0.0;
}
else if( epsilon_meas >= epsilon_max )
{
*S_fail_obs = sqrt( 2.0 * LogQ );
*S_pass_obs = 0.0;
}
else
{
*S_fail_obs = 0.0;
*S_pass_obs = sqrt( 2.0 * LogQ );
}
}
| void PoissionLogLikelihoodRatio | ( | double | , |
| double | , | ||
| double | , | ||
| double | , | ||
| double * | , | ||
| double * | |||
| ) |
Definition at line 76 of file QStatisticalTests.cc.
References runTheMatrix::data, and mathSSE::sqrt().
{
/*--------------------------------------------------------------------------+
| Description: Log-likelihood Ratio for Poission PDF |
+--------------------------------------------------------------------------+
| Input to this function |
+--------------------------------------------------------------------------+
|double data, : The observed number of entries |
|double sigma, : The uncertainty on, data, the observed entries |
|double hypothesis, : The assumed hypothese, tested against data |
|double epsilon_max, : Maximum tolerance above fraction of fitted line |
|double epsilon_min, : Minimum tolerance below fraction of fitted line |
|double* S_fail_obs : uninitialised Significance of failure |
|double* S_pass_obs : uninitialised Significance of Success |
+--------------------------------------------------------------------------+
| Return values for this function |
+--------------------------------------------------------------------------+
|double* S_fail_obs : the observed Significance of failure |
|double* S_pass_obs : the observed Significance of Success |
+--------------------------------------------------------------------------+
| Author: Richard Cavanaugh, University of Florida |
| email: Richard.Cavanaugh@cern.ch |
| Creation Date: 14.Jan.2006 |
| Last Modified: 16.Jan.2006 |
| Comments: |
+--------------------------------------------------------------------------*/
double tolerance_min = hypothesis*(1.0-epsilon_min);
double tolerance_max = hypothesis*(1.0+epsilon_max);
*S_pass_obs = 0.0;
*S_fail_obs = 0.0;
if( data > tolerance_max )
{
double Nsig = data - tolerance_max;
double Nbak = tolerance_max;
double LogQ = (double) (Nsig + Nbak) *
Log( 1.0 + (double) Nsig / (double) Nbak ) - (double) Nsig;
*S_fail_obs = sqrt( 2.0 * LogQ );
}
else if( tolerance_min < data && data < tolerance_max )
{
if( data - hypothesis > 0.0 )
{
double Nsig = tolerance_max - data;
double Nbak = tolerance_max;
double LogQ = (double) (Nsig + Nbak) *
Log( 1.0 + (double) Nsig / (double) Nbak ) - (double) Nsig;
*S_pass_obs = sqrt( 2.0 * LogQ );
}
else
{
double Nsig = data - tolerance_min;
double Nbak = tolerance_min;
double LogQ = (double) (Nsig + Nbak) *
Log( 1.0 + (double) Nsig / (double) Nbak ) - (double) Nsig;
*S_pass_obs = sqrt( 2.0 * LogQ );
}
}
else // data < tolerance_min
{
double Nsig = tolerance_min - data;
double Nbak = tolerance_min;
double LogQ = (double) (Nsig + Nbak) *
Log( 1.0 + (double) Nsig / (double) Nbak ) - (double) Nsig;
*S_fail_obs = sqrt( 2.0 * LogQ );
}
}
1.7.3