Implements a mixture model of the inverse gumbel and the gauss distribution or a gaussian mixture. More...

#include <OpenMS/MATH/STATISTICS/PosteriorErrorProbabilityModel.h>

Inheritance diagram for PosteriorErrorProbabilityModel:

Public Member Functions
	PosteriorErrorProbabilityModel ()
	default constructor
virtual	~PosteriorErrorProbabilityModel ()
	Destructor.
bool	fit (std::vector< double > &search_engine_scores)
	fits the distributions to the data points(search_engine_scores). Estimated parameters for the distributions are saved in member variables. computeProbability can be used afterwards.
bool	fit (std::vector< double > &search_engine_scores, std::vector< double > &probabilities)
	fits the distributions to the data points(search_engine_scores) and writes the computed probabilites into the given vector (the second one).
void	fillDensities (std::vector< double > &x_scores, std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density)
	Writes the distributions densities into the two vectors for a set of scores. Incorrect_densities represent the incorreclty assigned seqeuences.
DoubleReal	computeMaxLikelihood (std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density)
	computes the Maximum Likelihood with a log-likelihood funciotn.
DoubleReal	one_minus_sum_post (std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density)
	sums (1 - posterior porbabilities)
DoubleReal	sum_post (std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density)
	sums posterior porbabilities
DoubleReal	sum_pos_x0 (std::vector< double > &x_scores, std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density)
	helper function for the EM algorithm (for fitting)
DoubleReal	sum_neg_x0 (std::vector< double > &x_scores, std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density)
	helper function for the EM algorithm (for fitting)
DoubleReal	sum_pos_sigma (std::vector< double > &x_scores, std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density, DoubleReal positive_mean)
	helper function for the EM algorithm (for fitting)
DoubleReal	sum_neg_sigma (std::vector< double > &x_scores, std::vector< DoubleReal > &incorrect_density, std::vector< DoubleReal > &correct_density, DoubleReal positive_mean)
	helper function for the EM algorithm (for fitting)
GaussFitter::GaussFitResult	getCorrectlyAssignedFitResult () const
	returns estimated parameters for correctly assigned sequences. Fit should be used before.
GaussFitter::GaussFitResult	getIncorrectlyAssignedFitResult () const
	returns estimated parameters for correctly assigned sequences. Fit should be used before.
DoubleReal	getNegativePrior () const
	returns the estimated negative prior probability.
DoubleReal	getGauss (DoubleReal x, const GaussFitter::GaussFitResult &params)
	computes the gaussian density at position x with parameters params.
DoubleReal	getGumbel (DoubleReal x, const GaussFitter::GaussFitResult &params)
	computes the gumbel density at position x with parameters params.
DoubleReal	computeProbability (DoubleReal score)
TextFile *	InitPlots (std::vector< double > &x_scores)
	initializes the plots
const String	getGumbelGnuplotFormula (const GaussFitter::GaussFitResult &params) const
	returns the gnuplot formula of the fitted gumbel distribution. Only x0 and sigma are used as local parameter alpha and scale parameter beta, respectively.
const String	getGaussGnuplotFormula (const GaussFitter::GaussFitResult &params) const
	returns the gnuplot formula of the fitted gauss distribution.
const String	getBothGnuplotFormula (const GaussFitter::GaussFitResult &incorrect, const GaussFitter::GaussFitResult &correct) const
	returns the gnuplot formula of the fitted mixture distribution.
void	plotTargetDecoyEstimation (std::vector< double > &target, std::vector< double > &decoy)
	plots the estimated distribution against target and decoy hits
DoubleReal	getSmallestScore ()
	returns the smallest score used in the last fit
Private Member Functions
PosteriorErrorProbabilityModel &	operator= (const PosteriorErrorProbabilityModel &rhs)
	assignment operator (not implemented)
	PosteriorErrorProbabilityModel (const PosteriorErrorProbabilityModel &rhs)
	Copy constructor (not implemented).
Private Attributes
GaussFitter::GaussFitResult	incorrectly_assigned_fit_param_
	stores parameters for incorrectly assigned sequences. If gumbel fit was used, A can be ignored. Furthermore, in this case, x0 and sigma are the local parameter alpha and scale parameter beta, respectively.
GaussFitter::GaussFitResult	correctly_assigned_fit_param_
	stores gauss parameters
DoubleReal	negative_prior_
	stores final prior probability for negative peptides
DoubleReal	max_incorrectly_
	peak of the incorrectly assigned sequences distribution
DoubleReal	max_correctly_
	peak of the gauss distribution (correctly assigned sequences)
DoubleReal	smallest_score_
	smallest score which was used for fitting the model
DoubleReal(PosteriorErrorProbabilityModel::*	calc_incorrect_ )(DoubleReal x, const GaussFitter::GaussFitResult &params)
	points to getGauss
DoubleReal(PosteriorErrorProbabilityModel::*	calc_correct_ )(DoubleReal x, const GaussFitter::GaussFitResult &params)
	points either to getGumbel or getGauss depending on whether on uses the gumbel or th gausian distribution for incorrectly assigned sequences.
const String(PosteriorErrorProbabilityModel::*	getNegativeGnuplotFormula_ )(const GaussFitter::GaussFitResult &params) const
	points either to getGumbelGnuplotFormula or getGaussGnuplotFormula depending on whether on uses the gumbel or th gausian distribution for incorrectly assigned sequences.
const String(PosteriorErrorProbabilityModel::*	getPositiveGnuplotFormula_ )(const GaussFitter::GaussFitResult &params) const
	points to getGumbelGnuplotFormula

Detailed Description

Implements a mixture model of the inverse gumbel and the gauss distribution or a gaussian mixture.

This class fits either a Gumbel distribution and a Gauss distribution to a set of data points or two Gaussian distributions using the EM algorithm. One can output the fit as a gnuplot formula using getGumbelGnuplotFormula() and getGaussGnuplotFormula() after fitting.

Note:: All paremters are stored in GaussFitResult. In the case of the gumbel distribution x0 and sigma represent the local parameter alpha and the scale parameter beta, respectively.

Parameters of this class are:

Name	Type	Default	Restrictions	Description
number_of_bins	int	100		Number of bins used for visualization. Only needed if each iteration step of the EM-Algorithm will be visualized
output_plots	string	false	true, false	If true every step of the EM-algorithm will be written to a file as a gnuplot formula
output_name	string			If output_plots is on, the output files will be saved in the following manner: scores.txt for the scores and which contains each step of the EM-algorithm e.g. output_name = /usr/home/OMSSA123 then /usr/home/OMSSA123_scores.txt, /usr/home/OMSSA123 will be written. If no directory is specified, e.g. instead of '/usr/home/OMSSA123' just OMSSA123, the files will be written into the working directory.
incorrectly_assigned	string	Gumbel	Gumbel, Gauss	for 'Gumbel', the Gumbel distribution is used to plot incorrectly assigned sequences. For 'Gauss', the Gauss distribution is used.

Note:

If a section name is documented, the documentation is displayed as tooltip.
Advanced parameter names are italic.

Constructor & Destructor Documentation

PosteriorErrorProbabilityModel ( )

default constructor

virtual ~PosteriorErrorProbabilityModel ( ) [virtual]

Destructor.

PosteriorErrorProbabilityModel ( const PosteriorErrorProbabilityModel & rhs ) [private]

Copy constructor (not implemented).

Member Function Documentation

DoubleReal computeMaxLikelihood	(	std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density
	)

computes the Maximum Likelihood with a log-likelihood funciotn.

DoubleReal computeProbability ( DoubleReal score )

Returns the computed posterior error probability for a given score.

Note:: : fit has to be used before using this function. Otherwise this function will compute nonsense.

void fillDensities	(	std::vector< double > &	x_scores,
		std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density
	)

Writes the distributions densities into the two vectors for a set of scores. Incorrect_densities represent the incorreclty assigned seqeuences.

bool fit	(	std::vector< double > &	search_engine_scores,
		std::vector< double > &	probabilities
	)

fits the distributions to the data points(search_engine_scores) and writes the computed probabilites into the given vector (the second one).

Parameters:

	search_engine_scores	a vector which holds the data points
	probabilities	a vector which holds the probability for each data point after running this function. If it has some content it will be overwritten.

Returns:: true if algorithm has run thourgh. Else false will be returned. In that case no plot and no probabilities are calculated.

Note:: the vectors are sorted from smallest to biggest value!

bool fit ( std::vector< double > & search_engine_scores )

fits the distributions to the data points(search_engine_scores). Estimated parameters for the distributions are saved in member variables. computeProbability can be used afterwards.

Parameters:

search_engine_scores

a vector which holds the data points

Returns:: true if algorithm has run thourgh. Else false will be returned. In that case no plot and no probabilities are calculated.

Note:: the vector is sorted from smallest to biggest value!

const String getBothGnuplotFormula	(	const GaussFitter::GaussFitResult &	incorrect,
		const GaussFitter::GaussFitResult &	correct
	)			const

returns the gnuplot formula of the fitted mixture distribution.

GaussFitter::GaussFitResult getCorrectlyAssignedFitResult ( ) const [inline]

returns estimated parameters for correctly assigned sequences. Fit should be used before.

DoubleReal getGauss	(	DoubleReal	x,
		const GaussFitter::GaussFitResult &	params
	)			`[inline]`

computes the gaussian density at position x with parameters params.

References GaussFitter::GaussFitResult::A, GaussFitter::GaussFitResult::sigma, and GaussFitter::GaussFitResult::x0.

const String getGaussGnuplotFormula ( const GaussFitter::GaussFitResult & params ) const

returns the gnuplot formula of the fitted gauss distribution.

DoubleReal getGumbel	(	DoubleReal	x,
		const GaussFitter::GaussFitResult &	params
	)			`[inline]`

computes the gumbel density at position x with parameters params.

References GaussFitter::GaussFitResult::sigma, and GaussFitter::GaussFitResult::x0.

const String getGumbelGnuplotFormula ( const GaussFitter::GaussFitResult & params ) const

returns the gnuplot formula of the fitted gumbel distribution. Only x0 and sigma are used as local parameter alpha and scale parameter beta, respectively.

GaussFitter::GaussFitResult getIncorrectlyAssignedFitResult ( ) const [inline]

returns estimated parameters for correctly assigned sequences. Fit should be used before.

DoubleReal getNegativePrior ( ) const [inline]

returns the estimated negative prior probability.

DoubleReal getSmallestScore ( ) [inline]

returns the smallest score used in the last fit

TextFile* InitPlots ( std::vector< double > & x_scores )

initializes the plots

DoubleReal one_minus_sum_post	(	std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density
	)

sums (1 - posterior porbabilities)

PosteriorErrorProbabilityModel& operator= ( const PosteriorErrorProbabilityModel & rhs ) [private]

assignment operator (not implemented)

void plotTargetDecoyEstimation	(	std::vector< double > &	target,
		std::vector< double > &	decoy
	)

plots the estimated distribution against target and decoy hits

DoubleReal sum_neg_sigma	(	std::vector< double > &	x_scores,
		std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density,
		DoubleReal	positive_mean
	)

helper function for the EM algorithm (for fitting)

DoubleReal sum_neg_x0	(	std::vector< double > &	x_scores,
		std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density
	)

helper function for the EM algorithm (for fitting)

DoubleReal sum_pos_sigma	(	std::vector< double > &	x_scores,
		std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density,
		DoubleReal	positive_mean
	)

helper function for the EM algorithm (for fitting)

DoubleReal sum_pos_x0	(	std::vector< double > &	x_scores,
		std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density
	)

helper function for the EM algorithm (for fitting)

DoubleReal sum_post	(	std::vector< DoubleReal > &	incorrect_density,
		std::vector< DoubleReal > &	correct_density
	)

sums posterior porbabilities

Member Data Documentation

DoubleReal(PosteriorErrorProbabilityModel::* calc_correct_)(DoubleReal x, const GaussFitter::GaussFitResult &params) [private]

points either to getGumbel or getGauss depending on whether on uses the gumbel or th gausian distribution for incorrectly assigned sequences.

DoubleReal(PosteriorErrorProbabilityModel::* calc_incorrect_)(DoubleReal x, const GaussFitter::GaussFitResult &params) [private]

points to getGauss

GaussFitter::GaussFitResult correctly_assigned_fit_param_ [private]

stores gauss parameters

const String(PosteriorErrorProbabilityModel::* getNegativeGnuplotFormula_)(const GaussFitter::GaussFitResult &params) const [private]

points either to getGumbelGnuplotFormula or getGaussGnuplotFormula depending on whether on uses the gumbel or th gausian distribution for incorrectly assigned sequences.

const String(PosteriorErrorProbabilityModel::* getPositiveGnuplotFormula_)(const GaussFitter::GaussFitResult &params) const [private]

points to getGumbelGnuplotFormula

GaussFitter::GaussFitResult incorrectly_assigned_fit_param_ [private]

stores parameters for incorrectly assigned sequences. If gumbel fit was used, A can be ignored. Furthermore, in this case, x0 and sigma are the local parameter alpha and scale parameter beta, respectively.

DoubleReal max_correctly_ [private]

peak of the gauss distribution (correctly assigned sequences)

DoubleReal max_incorrectly_ [private]

peak of the incorrectly assigned sequences distribution

DoubleReal negative_prior_ [private]

stores final prior probability for negative peptides

DoubleReal smallest_score_ [private]

smallest score which was used for fitting the model

OpenMS / TOPP release 1.10.0

Documentation generated on Thu Mar 7 2013 09:43:00 using doxygen 1.7.1

PosteriorErrorProbabilityModel Class Reference [Math]

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

PosteriorErrorProbabilityModel Class Reference
[Math]