Multivariate multiple regression with parameter expansion. More...

#include <MuGen.h>

Inheritance diagram for BetaGrpPEX:

Collaboration diagram for BetaGrpPEX:

Public Member Functions
	BetaGrpPEX ()
	Default constructor.

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, RanIndex &up, const int &nThr)
	Simple constructor with a prior index. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, RanIndex &low, RanIndex &up, const int &nThr)
	Simple constructor with a prior index and replication. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, RanIndex &up, const string &outFlNam, const int &nThr)
	Simple constructor with a prior index and output file name. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Simple constructor with a prior index, replication and output file name. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, const double &absLab, RanIndex &up, const int &nThr)
	Missing data constructor with a prior index. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, const double &absLab, RanIndex &low, RanIndex &up, const int &nThr)
	Missing data constructor with a prior index and replication. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, const double &absLab, RanIndex &up, const string &outFlNam, const int &nThr)
	Missing data constructor with a prior index and output file name. More...

	BetaGrpPEX (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &Spr, const double &absLab, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Missing data constructor with a prior index, replication and output file name. More...

	BetaGrpPEX (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Spr, const double &Nmul, const double &rSqMax, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor. More...

	BetaGrpPEX (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Spr, const double &Nmul, const double &rSqMax, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor with replication. More...

	BetaGrpPEX (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Spr, const double &Nmul, const double &rSqMax, const double &absLab, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor with missing predictor data. More...

	BetaGrpPEX (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Spr, const double &Nmul, const double &rSqMax, const double &absLab, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor with missing predictor data and replication. More...

virtual	~BetaGrpPEX ()
	Destructor.

virtual const gsl_matrix *	fMat () const
	Access adjusted fitted value matrix. More...

virtual void	save ()
	Save adjusted values. More...

virtual void	save (const string &outFlNam)
	Save adjusted values to named file. More...

virtual void	save (const SigmaI &SigI)
	Save adjusted values with the adjusted covariance matrix. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const SigmaI &SigIp)
	Gaussian likelihood, 0-mean Gaussian prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const SigmaI &SigIp)
	Student- \(t\) likelihood, 0-mean Gaussian prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const Qgrp &qPr, const SigmaI &SigIp)
	Gaussian likelihood, 0-mean Student- \(t\) prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const Qgrp &qPr, const SigmaI &SigIp)
	Student- \(t\) likelihood, 0-mean Student- \(t\) prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const Grp &muPr, const SigmaI &SigIp)
	Gaussian likelihood, non-zero mean Gaussian prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const Grp &muPr, const SigmaI &SigIp)
	Student- \(t\) likelihood, non-zero mean Gaussian prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const Grp &muPr, const Qgrp &qPr, const SigmaI &SigIp)
	Gaussian likelihood, non-zero mean Student- \(t\) prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const Grp &muPr, const Qgrp &qPr, const SigmaI &SigIp)
	Student- \(t\) likelihood, non-zero mean Student- \(t\) prior. More...

Public Member Functions inherited from BetaGrpFt
	BetaGrpFt ()
	Default constructor.

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const int &nThr)
	Simple constructor. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, RanIndex &up, const int &nThr)
	Simple constructor with a prior index. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, RanIndex &low, RanIndex &up, const int &nThr)
	Simple constructor with a prior index and replication. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const string &outFlNam, const int &nThr)
	Simple constructor with output file name. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, RanIndex &up, const string &outFlNam, const int &nThr)
	Simple constructor with a prior index and output file name. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Simple constructor with a prior index, replication and output file name. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &absLab, const int &nThr)
	Missing data constructor. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &absLab, RanIndex &up, const int &nThr)
	Missing data constructor with a prior index. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &absLab, RanIndex &low, RanIndex &up, const int &nThr)
	Missing data constructor with a prior index and replication. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &absLab, const string &outFlNam, const int &nThr)
	Missing data constructor with output file name. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &absLab, RanIndex &up, const string &outFlNam, const int &nThr)
	Missing data constructor with a prior index and output file name. More...

	BetaGrpFt (const Grp &rsp, const string &predFlNam, const size_t &Npred, const double &absLab, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Missing data constructor with a prior index, replication and output file name. More...

	BetaGrpFt (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Nmul, const double &rSqMax, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor. More...

	BetaGrpFt (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Nmul, const double &rSqMax, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor with replication. More...

	BetaGrpFt (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Nmul, const double &rSqMax, const double &absLab, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor with missing predictor data. More...

	BetaGrpFt (const Grp &rsp, const SigmaI &SigI, const string &predFlNam, const size_t &Npred, const double &Nmul, const double &rSqMax, const double &absLab, RanIndex &low, RanIndex &up, const string &outFlNam, const int &nThr)
	Selection constructor with missing predictor data and replication. More...

virtual	~BetaGrpFt ()
	Destructor.

	BetaGrpFt (const BetaGrpFt &mG)
	Copy constructor. More...

BetaGrpFt &	operator= (const BetaGrpFt &mG)
	Assignment operator. More...

void	dump ()
	Dump to a file. More...

double	lnOddsRat (const Grp &y, const SigmaI &SigI, const size_t i) const
	Log-odds ratio. More...

void	update (const Grp &dat, const SigmaI &SigIm)
	Gaussian likelihood, improper prior. More...

void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm)
	Student- \(t\) likelihood, improper prior. More...

Public Member Functions inherited from Grp
virtual	~Grp ()
	Destructor.

virtual void	save (const string &outMuFlNam, const string &outSigFlNam, const SigmaI &SigI)
	Joint save. More...

virtual void	save (const Grp &y, const SigmaI &SigI)
	Save with data and inverse-covariance. More...

void	mhlSave (const string &outFlNam, const SigmaI SigI)
	Save Mahalanobis distance. More...

const vector< MVnorm * > &	dataVec () const
	Get vector of row pointers. More...

virtual const gsl_matrix *	dMat () const
	Access the value matrix. More...

const size_t	Ndata () const
	Get number of rows. More...

const size_t	phenD () const
	Get number of traits. More...

const MVnorm *	operator[] (const size_t i) const
	Subscript operator. More...

MVnorm *	operator[] (const size_t i)
	Subscript operator. More...

virtual MuGrp	mean (RanIndex &grp)
	Group mean. More...

virtual const MuGrp	mean (RanIndex &grp) const
	Group mean. More...

virtual MuGrp	mean (RanIndex &grp, const Qgrp &q)
	Group weighted mean. More...

virtual const MuGrp	mean (RanIndex &grp, const Qgrp &q) const
	Group weighted mean. More...

void	center ()
	Center the value matrix. More...

Protected Member Functions
void	_finishConstruct (const double &Spr)
	Finish construction. More...

void	_finishFitted ()
	Adjusted matrix calculation. More...

void	_updateAfitted ()
	Calculate redundant parameter fitted values. More...

	BetaGrpPEX (const double &Spr)
	Finishing constructor. More...

Protected Member Functions inherited from BetaGrpFt
virtual void	_updateFitted ()
	Update fitted values.

void	_rankPred (const gsl_matrix y, const SigmaI &SigI, gsl_vector XtX, gsl_permutation *prm)
	Rank predictors. More...

void	_rankPred (const gsl_matrix y, const SigmaI &SigI, const double &absLab, gsl_vector XtX, gsl_permutation *prm)
	Rank predictors with missing data. More...

void	_ldToss (const gsl_vector var, const gsl_permutation prm, const double &rSqMax, const size_t &Npck, vector< vector< size_t > > &idx, vector< vector< size_t > > &rLd, gsl_matrix *Xpck)
	Testing candidates for correlation. More...

virtual double	_MGkernel (const Grp &dat, const SigmaI &SigI) const
	Gaussian kernel. More...

virtual double	_MGkernel (const Grp &dat, const SigmaI &SigI, const size_t &prInd) const
	Gaussian kernel dropping one predictor. More...

Protected Member Functions inherited from Grp
	Grp ()

Protected Attributes
gsl_matrix *	_tSigIAt
	Scaled inverse-covariance. More...

Apex	_A
	Multiplicative redundant parameter.

vector< vector< double > >	_ftA
	Fitted values. More...

gsl_matrix *	_fittedAllAdj
	Adjusted fitted matrix. More...

Protected Attributes inherited from BetaGrpFt
vector< vector< double > >	_fittedEach
	Partial fitted value matrices. More...

gsl_matrix *	_fittedAll
	Matrix of fitted values. More...

gsl_matrix *	_valueSum
	Sample storage matrix. More...

gsl_matrix *	_Xmat
	Predictor matrix. More...

int	_nThr
	Number of threads.

double	_numSaves
	Number of saves. More...

Protected Attributes inherited from Grp
vector< MVnorm * >	_theta
	Vector of pointers to value rows. More...

gsl_matrix *	_valueMat
	Value matrix. More...

RanIndex *	_lowLevel
	Lower level index. More...

RanIndex *	_upLevel
	Upper level index. More...

vector< gsl_rng * >	_rV
	Vector of PNG pointers. More...

string	_outFlNam
	Name of the output file.

Detailed Description

Multivariate multiple regression with parameter expansion.

Implements multiplicative prameter expansion as in MuGrpPEX, but for regression coefficients. The "raw" value matrix \( \boldsymbol{\Xi} \) contains regression coefficients on the unadjusted scale (see MuGrpPEX for explanation of these terms). The adjusted regression coefficient matrix is not calculated, but fMat() point to the adjusted fitted value matrix \( \boldsymbol{X \Xi A} \). Regression models with this method must have a prior, typically one with mean zero.

Constructor & Destructor Documentation

◆ BetaGrpPEX() [1/13]

BetaGrpPEX::BetaGrpPEX ( const double & Spr )

inlineprotected

Finishing constructor.

For use in BetaGrpPCpex.

Parameters

[in] double& prior inverse variance for \( \boldsymbol{A} \)

◆ BetaGrpPEX() [2/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		RanIndex &	up,
		const int &	nThr
	)

inline

Simple constructor with a prior index.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Number of rows of the predictor is equal to the number of rows in the response (no replication).

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	RanIndex&	index to the prior
[in]	int&	number of threads

◆ BetaGrpPEX() [3/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		RanIndex &	low,
		RanIndex &	up,
		const int &	nThr
	)

inline

Simple constructor with a prior index and replication.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Number of rows of the predictor is equal to the number of groups in the lower (data) index. The number of rows in the response is equal to the number of elements in the low index

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	RanIndex&	replication index
[in]	RanIndex&	index to the prior
[in]	int&	number of threads

◆ BetaGrpPEX() [4/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Simple constructor with a prior index and output file name.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Number of rows of the predictor is equal to the number of rows in the response (no replication).

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [5/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		RanIndex &	low,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Simple constructor with a prior index, replication and output file name.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Number of rows of the predictor is equal to the number of groups in the lower (data) index. The number of rows in the response is equal to the number of elements in the low index

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	RanIndex&	replication index
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [6/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	absLab,
		RanIndex &	up,
		const int &	nThr
	)

inline

Missing data constructor with a prior index.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Predictor has missing data labeled by the provided value. Number of rows of the predictor is equal to the number of rows in the response (no replication).

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	missing data label
[in]	RanIndex&	index to the prior
[in]	int&	number of threads

◆ BetaGrpPEX() [7/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	absLab,
		RanIndex &	low,
		RanIndex &	up,
		const int &	nThr
	)

inline

Missing data constructor with a prior index and replication.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Predictor has missing data labeled by the provided value. Number of rows of the predictor is equal to the number of groups in the lower (data) index. The number of rows in the response is equal to the number of elements in the low index

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	missing data label
[in]	RanIndex&	replication index
[in]	RanIndex&	index to the prior
[in]	int&	number of threads

◆ BetaGrpPEX() [8/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	absLab,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Missing data constructor with a prior index and output file name.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Predictor has missing data labeled by the provided value. Number of rows of the predictor is equal to the number of rows in the response (no replication).

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	missing data label
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [9/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	absLab,
		RanIndex &	low,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Missing data constructor with a prior index, replication and output file name.

Reads the predictor from a file and initiates regression coefficients using the provided response data. Predictor has missing data labeled by the provided value. Number of rows of the predictor is equal to the number of groups in the lower (data) index. The number of rows in the response is equal to the number of elements in the low index

Parameters

[in]	Grp&	response
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	missing data label
[in]	RanIndex&	replication index
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [10/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const SigmaI &	SigI,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	Nmul,
		const double &	rSqMax,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Selection constructor.

Unlike the previous constructors, selection-type constructors pre-screen predictors for association with any of the traits (using Hoteling-like multi-trait statistics), and keeps only the specified proportion in the model. The candidate predictors are further screened for between-predictor correlation and only ones with \( r^2 \) below the provided threshold are kept in the model. This among-predictor correlation often arises in SNP data, where there is likage disequilbrium (LD) among markers. Once the predictors are picked, the updating proceeds like for other BetaGrpFt objects, i.e. the set remains the same (unlike variable selection).

Warning: This set of models is still experimental and has not been extenisively tested. For example, it seems clear that the rest of the model has to be pre-run to convergence before initializing this kind of object. Furthermore, the initializeing predictor probably has to be a mean of several ( \( \sim 50 \)) MCMC samples.

Parameters

[in]	Grp&	response
[in]	SigmaI&	data inverse-covariance
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	fraction of predictors to retain, as a fraction of the number of data points
[in]	double&	\( r^2 \) cut-off for among-predictor correlation
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [11/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const SigmaI &	SigI,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	Nmul,
		const double &	rSqMax,
		RanIndex &	low,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Selection constructor with replication.

Unlike the previous constructors, selection-type constructors pre-screen predictors for association with any of the traits (using Hoteling-like multi-trait statistics), and keeps only the specified proportion in the model. The candidate predictors are further screened for between-predictor correlation and only ones with \( r^2 \) below the provided threshold are kept in the model. This among-predictor correlation often arises in SNP data, where there is likage disequilbrium (LD) among markers. Once the predictors are picked, the updating proceeds like for other BetaGrpFt objects, i.e. the set remains the same (unlike variable selection).

Warning: This set of models is still experimental and has not been extenisively tested. For example, it seems clear that the rest of the model has to be pre-run to convergence before initializing this kind of object. Furthermore, the initializeing predictor probably has to be a mean of several ( \( \sim 50 \)) MCMC samples.

Parameters

[in]	Grp&	response
[in]	SigmaI&	data inverse-covariance
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	fraction of predictors to retain, as a fraction of the number of data points
[in]	double&	\( r^2 \) cut-off for among-predictor correlation
[in]	RanIndex&	replication index
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [12/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const SigmaI &	SigI,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	Nmul,
		const double &	rSqMax,
		const double &	absLab,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Selection constructor with missing predictor data.

Unlike the previous constructors, selection-type constructors pre-screen predictors for association with any of the traits (using Hoteling-like multi-trait statistics), and keeps only the specified proportion in the model. The candidate predictors are further screened for between-predictor correlation and only ones with \( r^2 \) below the provided threshold are kept in the model. This among-predictor correlation often arises in SNP data, where there is likage disequilbrium (LD) among markers. Once the predictors are picked, the updating proceeds like for other BetaGrpFt objects, i.e. the set remains the same (unlike variable selection). Missing predictor data are filled in by mean imputation.

Warning: This set of models is still experimental and has not been extenisively tested. For example, it seems clear that the rest of the model has to be pre-run to convergence before initializing this kind of object. Furthermore, the initializeing predictor probably has to be a mean of several ( \( \sim 50 \)) MCMC samples.

Parameters

[in]	Grp&	response
[in]	SigmaI&	data inverse-covariance
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	fraction of predictors to retain, as a fraction of the number of data points
[in]	double&	\( r^2 \) cut-off for among-predictor correlation
[in]	double&	missing data label
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

◆ BetaGrpPEX() [13/13]

BetaGrpPEX::BetaGrpPEX	(	const Grp &	rsp,
		const SigmaI &	SigI,
		const string &	predFlNam,
		const size_t &	Npred,
		const double &	Spr,
		const double &	Nmul,
		const double &	rSqMax,
		const double &	absLab,
		RanIndex &	low,
		RanIndex &	up,
		const string &	outFlNam,
		const int &	nThr
	)

inline

Selection constructor with missing predictor data and replication.

Unlike the previous constructors, selection-type constructors pre-screen predictors for association with any of the traits (using Hoteling-like multi-trait statistics), and keeps only the specified proportion in the model. The candidate predictors are further screened for between-predictor correlation and only ones with \( r^2 \) below the provided threshold are kept in the model. This among-predictor correlation often arises in SNP data, where there is likage disequilbrium (LD) among markers. Once the predictors are picked, the updating proceeds like for other BetaGrpFt objects, i.e. the set remains the same (unlike variable selection). Missing predictor data are filled in by mean imputation.

Warning: This set of models is still experimental and has not been extenisively tested. For example, it seems clear that the rest of the model has to be pre-run to convergence before initializing this kind of object. Furthermore, the initializeing predictor probably has to be a mean of several ( \( \sim 50 \)) MCMC samples.

Parameters

[in]	Grp&	response
[in]	SigmaI&	data inverse-covariance
[in]	srting&	predictor file name
[in]	size_t&	number of predictors
[in]	double&	prior inverse variance for \( \boldsymbol{A} \)
[in]	double&	fraction of predictors to retain, as a fraction of the number of data points
[in]	double&	\( r^2 \) cut-off for among-predictor correlation
[in]	double&	missing data label
[in]	RanIndex&	replication index
[in]	RanIndex&	index to the prior
[in]	string&	output file name
[in]	int&	number of threads

Member Function Documentation

◆ _finishConstruct()

void BetaGrpPEX::_finishConstruct ( const double & Spr )

protected

Finish construction.

Most of the construction tasks are handled by the parent class. This function sets up the PEX portion of the class.

Parameters

[in] double& prior inverse variance for \( \boldsymbol{A} \)

◆ _finishFitted()

void BetaGrpPEX::_finishFitted ( )

protected

Adjusted matrix calculation.

Calculates adjusted fitted matrix and all sub-matrices.

◆ fMat()

virtual const gsl_matrix* BetaGrpPEX::fMat ( ) const

inlinevirtual

Access adjusted fitted value matrix.

Returns: gsl_matrix* pointer to the adjusted-value fitted matrix

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ save() [1/3]

void BetaGrpPEX::save ( )

virtual

Save adjusted values.

Appends the current adjusted mean values to the file whose name was set during construction.

Reimplemented from Grp.

Reimplemented in BetaGrpPCpex.

◆ save() [2/3]

void BetaGrpPEX::save ( const SigmaI & SigI )

virtual

Save adjusted values with the adjusted covariance matrix.

Appends the current adjusted mean values and prior covariance matrix to the files whose names were set during construction.

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ save() [3/3]

void BetaGrpPEX::save ( const string & outFlNam )

virtual

Save adjusted values to named file.

Appends the current adjusted mean values to the named file.

Parameters

[in] string& file name

Reimplemented from Grp.

Reimplemented in BetaGrpPCpex.

◆ update() [1/8]

void BetaGrpPEX::update	(	const Grp &	,
		const Qgrp &	,
		const SigmaI &	,
		const Grp &	,
		const Qgrp &	,
		const SigmaI &
	)

virtual

Student- \(t\) likelihood, non-zero mean Student- \(t\) prior.

For the relationship to work properly, the _upLevel index of the focal object has to point to the rows of the prior mean matrix. This is the matrix addressed by dMat(). The relationship to the data is dependent on the derived class.

Parameters

[in]	Grp&	data
[in]	Qgrp&	Student- \(t\) weight parameter for the data
[in]	SigmaI&	data inverse-covariance
[in]	Grp&	prior mean
[in]	Qgrp&	Student- \(t\) weight parameter for the prior
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [2/8]

void BetaGrpPEX::update	(	const Grp &	,
		const Qgrp &	,
		const SigmaI &	,
		const Grp &	,
		const SigmaI &
	)

virtual

Student- \(t\) likelihood, non-zero mean Gaussian prior.

For the relationship to work properly, the _upLevel index of the focal object has to point to the rows of the prior mean matrix. This is the matrix addressed by dMat(). The relationship to the data is dependent on the derived class.

Parameters

[in]	Grp&	data
[in]	Qgrp&	Student- \(t\) weight parameter for the data
[in]	SigmaI&	data inverse-covariance
[in]	Grp&	prior mean
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [3/8]

void BetaGrpPEX::update	(	const Grp &	,
		const Qgrp &	,
		const SigmaI &	,
		const Qgrp &	,
		const SigmaI &
	)

virtual

Student- \(t\) likelihood, 0-mean Student- \(t\) prior.

Parameters

[in]	Grp&	data
[in]	Qgrp&	Student- \(t\) weight parameter for the data
[in]	SigmaI&	data inverse-covariance
[in]	Qgrp&	Student- \(t\) weight parameter for the prior
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [4/8]

void BetaGrpPEX::update	(	const Grp &	,
		const Qgrp &	,
		const SigmaI &	,
		const SigmaI &
	)

virtual

Student- \(t\) likelihood, 0-mean Gaussian prior.

Parameters

[in]	Grp&	data
[in]	Qgrp&	Student- \(t\) weight parameter for data
[in]	SigmaI&	data inverse-covariance
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [5/8]

void BetaGrpPEX::update	(	const Grp &	,
		const SigmaI &	,
		const Grp &	,
		const Qgrp &	,
		const SigmaI &
	)

virtual

Gaussian likelihood, non-zero mean Student- \(t\) prior.

For the relationship to work properly, the _upLevel index of the focal object has to point to the rows of the prior mean matrix. This is the matrix addressed by dMat(). The relationship to the data is dependent on the derived class.

Parameters

[in]	Grp&	data
[in]	SigmaI&	data inverse-covariance
[in]	Grp&	prior mean
[in]	Qgrp&	Student- \(t\) weight parameter for the prior
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [6/8]

void BetaGrpPEX::update	(	const Grp &	,
		const SigmaI &	,
		const Grp &	,
		const SigmaI &
	)

virtual

Gaussian likelihood, non-zero mean Gaussian prior.

For the relationship to work properly, the _upLevel index of the focal object has to point to the rows of the prior mean matrix. This is the matrix addressed by dMat(). The relationship to the data is dependent on the derived class.

Parameters

[in]	Grp&	data
[in]	SigmaI&	data inverse-covariance
[in]	Grp&	prior mean
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [7/8]

void BetaGrpPEX::update	(	const Grp &	,
		const SigmaI &	,
		const Qgrp &	,
		const SigmaI &
	)

virtual

Gaussian likelihood, 0-mean Student- \(t\) prior.

Parameters

[in]	Grp&	data
[in]	SigmaI&	data inverse-covariance
[in]	Qgrp&	Student- \(t\) weight parameter for the prior
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

◆ update() [8/8]

void BetaGrpPEX::update	(	const Grp &	,
		const SigmaI &	,
		const SigmaI &
	)

virtual

Gaussian likelihood, 0-mean Gaussian prior.

Parameters

[in]	Grp&	data
[in]	SigmaI&	data inverse-covariance
[in]	SigmaI&	prior inverse-covariance

Reimplemented from BetaGrpFt.

Reimplemented in BetaGrpPCpex.

Member Data Documentation

◆ _fittedAllAdj

gsl_matrix* BetaGrpPEX::_fittedAllAdj

protected

Adjusted fitted matrix.

The \( \boldsymbol{X \Xi A} \) matrix. The inherited _fittedAll matrix is \( \boldsymbol{X \Xi} \), i.e. the "raw" version.

◆ _ftA

vector<vector<double> > BetaGrpPEX::_ftA

protected

Fitted values.

Vector of vectorized individual fitted matrices \( \boldsymbol{\Xi}_{\cdot -m}\boldsymbol{A}_{-m\cdot} \).

◆ _tSigIAt

gsl_matrix* BetaGrpPEX::_tSigIAt

protected

Scaled inverse-covariance.

The matrix \( \left( \boldsymbol{\Sigma}^{-1}\boldsymbol{A}^T \right)^T \) that is common for sampling each row of the value matrix.

The documentation for this class was generated from the following files:

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ BetaGrpPEX() [1/13]

◆ BetaGrpPEX() [2/13]

◆ BetaGrpPEX() [3/13]

◆ BetaGrpPEX() [4/13]

◆ BetaGrpPEX() [5/13]

◆ BetaGrpPEX() [6/13]

◆ BetaGrpPEX() [7/13]

◆ BetaGrpPEX() [8/13]

◆ BetaGrpPEX() [9/13]

◆ BetaGrpPEX() [10/13]

◆ BetaGrpPEX() [11/13]

◆ BetaGrpPEX() [12/13]

◆ BetaGrpPEX() [13/13]

Member Function Documentation

◆ _finishConstruct()

◆ _finishFitted()

◆ fMat()

◆ save() [1/3]

◆ save() [2/3]

◆ save() [3/3]

◆ update() [1/8]

◆ update() [2/8]

◆ update() [3/8]

◆ update() [4/8]

◆ update() [5/8]

◆ update() [6/8]

◆ update() [7/8]

◆ update() [8/8]

Member Data Documentation

◆ _fittedAllAdj

◆ _ftA

◆ _tSigIAt