DecayStatistics.h File Reference

#include <cmath>
#include <vector>
#include <numeric>
#include <algorithm>
#include <iostream>
#include <cstring>

Go to the source code of this file.

Namespaces
namespace	statistics

Functions
void	init_fact ()
double	loggammaf (double t)
double	wcm (int C, double m)
double	wcm_p2s (int C, double mp, double ms)
double	Wcm_p2s (int *C, double *M, int Nchannels)
double	twoIstar_p2s (int *C, double *M, int Nchannels)
double	twoIstar (int *C, double *M, int Nchannels)
double	Wcm (int *C, double *M, int Nchannels)
double	statistics::neyman (double *data, double *model, int start, int stop)
double	statistics::poisson (double *data, double *model, int start, int stop)
double	statistics::pearson (double *data, double *model, int start, int stop)
double	statistics::gauss (double *data, double *model, int start, int stop)
double	statistics::cnp (double *data, double *model, int start, int stop)
double	statistics::sswr (double *data, double *model, double *data_noise, int start, int stop)
	Sum of squared weighted residuals.
double	statistics::chi2_counting (std::vector< double > &data, std::vector< double > &model, std::vector< double > &weights, int x_min=-1, int x_max=-1, const char *type="neyman")
	Computes different chi2 measures for counting data.

Function Documentation

init_fact()

void init_fact

(

)

Initialize an array containing pre-computed logratithms

loggammaf()

double loggammaf

(

double

t

)

Approximation of log(gamma function). See wikipedia

https://en.wikipedia.org/wiki/Gamma_function#The_log-gamma_function

Parameters

t	input of the gamma function

Returns

approximation of the logarithm of the gamma function

twoIstar()

double twoIstar	(	int *	C,
		double *	M,
		int	Nchannels )

Compute overall 2I* for Cp & Cs

This function computes 2I* for Cp and Cs. Cp and Cs are the model signals in the parallel and perpendicular channel. Contrary to twoIstar_p2s the overall 2I* is the sum of 2I* for Cp and Cs.

Parameters

C	array of experimental counts in Jordi format
M	array model function in Jordi format
Nchannels	number of micro time channels in parallel and perpendicular (half the number of elements in C and M).

Returns

2I* for Cp & Cs

twoIstar_p2s()

double twoIstar_p2s	(	int *	C,
		double *	M,
		int	Nchannels )

Compute overall 2I* for Cp + 2Cs

This function computes the overall 2I* for the model function Cp + 2Cs that is computed by parallel signal (Cp) and the perpendicular signal (Cs). For the definition of function 2I* see "An Experimental Comparison of the Maximum Likelihood Estimation and Nonlinear Least-Squares Fluorescence Lifetime Analysis of Single Molecules, Michael Maus, Mircea Cotlet, Johan Hofkens, Thomas Gensch, Frans C. De Schryver, J. Schaffer, and C. A. M. Seidel, Anal. Chem. 2001, 73, 9, 2078–2086".

Parameters

C	array of experimental counts in Jordi format
M	array model function in Jordi format
Nchannels	number of micro time channels in parallel and perpendicular (half the number of elements in C and M).

Returns

2I* for Cp + 2Cs

Wcm()

double Wcm	(	int *	C,
		double *	M,
		int	Nchannels )

Compute overall -log-likelihood for Cp & Cs

Parameters

C	array of experimental counts in Jordi format
M	array model function in Jordi format
Nchannels	number of micro time channels in parallel and perpendicular (half the number of elements in C and M).

Returns

-log-likelihood for Cp & Cs

wcm()

double wcm	(	int	C,
		double	m )

log-likelihood w(C|m) for Cp + 2Cs

Parameters

C	number of counts in channel
m	model function

Returns

log-likelihood w(C|m) for Cp + 2Cs

Wcm_p2s()

double Wcm_p2s	(	int *	C,
		double *	M,
		int	Nchannels )

Compute the overall -log-likelihood for Cp + 2Cs for all micro time channels

Parameters

C	array of experimental counts in Jordi format
M	array model function in Jordi format
Nchannels	number of micro time channels in parallel and perpendicular (half the number of elements in C and M).

Returns

-log-likelihood for Cp + 2Cs for all micro time channels

wcm_p2s()

double wcm_p2s	(	int	C,
		double	mp,
		double	ms )

Compute the -log-likelihood for Cp + 2Cs of a single micro time channel.

Compute score of model counts in a parallel and perpendicular detection channel and the experimental counts for a micro time channel.

This function computes a score for the experimental counts (C) in a channel where the experimental counts were computed by the sum of the counts in the parallel (P) and the perpendicular (S) channel by the equation C = P + 2 S.

This function considers that the number of counts C = P + 2S is not Poissonian. The score relates to a maximum likelihood function.

Parameters

C	number of experimental counts (P + 2 S) in a micro time channel
mp	number of counts of the model in parallel detection channel
ms	number of counts of the model in the perpendicular detection channel

Returns