path: root/param.cpp

/*
    Genome-wide Efficient Mixed Model Association (GEMMA)
    Copyright (C) 2011  Xiang Zhou

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

#include <iostream>
#include <fstream>
#include <string>
#include <cstring>
#include <sys/stat.h>
#include <cmath>
#include <algorithm>


#ifdef FORCE_FLOAT
#include "param_float.h"
#include "io_float.h"
#else
#include "param.h"
#include "io.h"
#endif

using namespace std;





PARAM::PARAM(void):	
mode_silence (false), a_mode (0), k_mode(1), d_pace (100000),
file_out("result"), path_out("./output/"),
miss_level(0.05), maf_level(0.01), hwe_level(0), r2_level(0.9999),
l_min(1e-5), l_max(1e5), n_region(10),p_nr(0.001),em_prec(0.0001),nr_prec(0.0001),em_iter(10000),nr_iter(100),crt(0),
pheno_mean(0),
h_min(-1), h_max(-1),	h_scale(-1),
rho_min(0.0), rho_max(1.0),	rho_scale(-1),
logp_min(0.0), logp_max(0.0), logp_scale(-1),
s_min(0), s_max(300),
w_step(100000),	s_step(1000000),
r_pace(10), w_pace(1000),
n_accept(0),
n_mh(10),
geo_mean(2000.0),
randseed(-1),
error(false),
  n_cvt(1), n_vc(1),
time_total(0.0), time_G(0.0), time_eigen(0.0), time_UtX(0.0), time_UtZ(0.0), time_opt(0.0), time_Omega(0.0)
{}


//read files
//obtain ns_total, ng_total, ns_test, ni_test
void PARAM::ReadFiles (void) 
{
	string file_str;
	if (!file_mk.empty()) {				
	  if (CountFileLines (file_mk, n_vc)==false) {error=true;}
	}
	
	if (!file_snps.empty()) {
		if (ReadFile_snps (file_snps, setSnps)==false) {error=true;}
	} else {
		setSnps.clear();
	}
	
	//for prediction
	if (!file_epm.empty()) {
		if (ReadFile_est (file_epm, est_column, mapRS2est)==false) {error=true;}
		
		if (!file_bfile.empty()) {
			file_str=file_bfile+".bim";
			if (ReadFile_bim (file_str, snpInfo)==false) {error=true;}		
			
			file_str=file_bfile+".fam";
			if (ReadFile_fam (file_str, indicator_pheno, pheno, mapID2num, p_column)==false) {error=true;}			
		}
		
		if (!file_geno.empty()) {			
			if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;}		
			
			if (CountFileLines (file_geno, ns_total)==false) {error=true;}	
		}
		
		if (!file_ebv.empty() ) {
			if (ReadFile_column (file_ebv, indicator_bv, vec_bv, 1)==false) {error=true;}
		}
		
		if (!file_log.empty() ) {
			if (ReadFile_log (file_log, pheno_mean)==false) {error=true;}
		}
		
		//convert indicator_pheno to indicator_idv
		int k=1;
		for (size_t i=0; i<indicator_pheno.size(); i++) {
			k=1;
			for (size_t j=0; j<indicator_pheno[i].size(); j++) {
				if (indicator_pheno[i][j]==0) {k=0;}
			}
			indicator_idv.push_back(k);
		}
		
		ns_test=0;
		
		return;
	}
	
	//read covariates before the genotype files
	if (!file_cvt.empty() ) {
		if (ReadFile_cvt (file_cvt, indicator_cvt, cvt, n_cvt)==false) {error=true;}

		if ((indicator_cvt).size()==0) {
			n_cvt=1;
		} 		
	} else {
		n_cvt=1;
	}

	//read genotype and phenotype file for plink format
	if (!file_bfile.empty()) {
		file_str=file_bfile+".bim";
		if (ReadFile_bim (file_str, snpInfo)==false) {error=true;}		
		
		file_str=file_bfile+".fam";
		if (ReadFile_fam (file_str, indicator_pheno, pheno, mapID2num, p_column)==false) {error=true;}
		
		//post-process covariates and phenotypes, obtain ni_test, save all useful covariates
		ProcessCvtPhen();
		
		//obtain covariate matrix
		gsl_matrix *W=gsl_matrix_alloc (ni_test, n_cvt);
		CopyCvt (W);
		
		file_str=file_bfile+".bed";
		if (ReadFile_bed (file_str, setSnps, W, indicator_idv, indicator_snp, snpInfo, maf_level, miss_level, hwe_level, r2_level, ns_test)==false) {error=true;}
		
		gsl_matrix_free(W);
		
		ns_total=indicator_snp.size();
	}
	
	//read genotype and phenotype file for bimbam format
	if (!file_geno.empty()) {
		//annotation file before genotype file
		if (!file_anno.empty() ) {
			if (ReadFile_anno (file_anno, mapRS2chr, mapRS2bp, mapRS2cM)==false) {error=true;}
		}

		//phenotype file before genotype file
		if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;}

		//post-process covariates and phenotypes, obtain ni_test, save all useful covariates
		ProcessCvtPhen();
		
		//obtain covariate matrix
		gsl_matrix *W=gsl_matrix_alloc (ni_test, n_cvt);
		CopyCvt (W);

		if (ReadFile_geno (file_geno, setSnps, W, indicator_idv, indicator_snp, maf_level, miss_level, hwe_level, r2_level, mapRS2chr, mapRS2bp, mapRS2cM, snpInfo, ns_test)==false) {error=true;}

		gsl_matrix_free(W);
		
		ns_total=indicator_snp.size();
	}
	
	if (!file_gene.empty()) {
		if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;}
		
		//convert indicator_pheno to indicator_idv
		int k=1;
		for (size_t i=0; i<indicator_pheno.size(); i++) {
			k=1;
			for (size_t j=0; j<indicator_pheno[i].size(); j++) {
				if (indicator_pheno[i][j]==0) {k=0;}
			}
			indicator_idv.push_back(k);
		}
		
		if (ReadFile_gene (file_gene, vec_read, snpInfo, ng_total)==false) {error=true;}	
	}
	
				
	//read is after gene file
	if (!file_read.empty() ) {
		if (ReadFile_column (file_read, indicator_read, vec_read, 1)==false) {error=true;}
		
		ni_test=0; 
		for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
			indicator_idv[i]*=indicator_read[i];
			ni_test+=indicator_idv[i];
		}
		
		if (ni_test==0) {
			error=true;
			cout<<"error! number of analyzed individuals equals 0. "<<endl;
			return;
		}
	}
	
	//for ridge prediction, read phenotype only
	if (file_geno.empty() && file_gene.empty() && !file_pheno.empty()) {
		if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;}	
				
		//post-process covariates and phenotypes, obtain ni_test, save all useful covariates
		ProcessCvtPhen();
	}

	return;
}






void PARAM::CheckParam (void) 
{	
	struct stat fileInfo;
	string str;
	
	//check parameters
	if (k_mode!=1 && k_mode!=2) {cout<<"error! unknown kinship/relatedness input mode: "<<k_mode<<endl; error=true;}
	if (a_mode!=1 && a_mode!=2 && a_mode!=3 && a_mode!=4 && a_mode!=5 && a_mode!=11 && a_mode!=12 && a_mode!=13 && a_mode!=21 && a_mode!=22 && a_mode!=31 && a_mode!=41 && a_mode!=42 && a_mode!=43 && a_mode!=51 && a_mode!=52 && a_mode!=53 && a_mode!=54 && a_mode!=61)   
	{cout<<"error! unknown analysis mode: "<<a_mode<<". make sure -gk or -eigen or -lmm or -bslmm or -predict is sepcified correctly."<<endl; error=true;}
	if (miss_level>1) {cout<<"error! missing level needs to be between 0 and 1. current value = "<<miss_level<<endl; error=true;}
	if (maf_level>0.5) {cout<<"error! maf level needs to be between 0 and 0.5. current value = "<<maf_level<<endl; error=true;}
	if (hwe_level>1) {cout<<"error! hwe level needs to be between 0 and 1. current value = "<<hwe_level<<endl; error=true;}
	if (r2_level>1) {cout<<"error! r2 level needs to be between 0 and 1. current value = "<<r2_level<<endl; error=true;}
	
	if (l_max<l_min) {cout<<"error! maximum lambda value must be larger than the minimal value. current values = "<<l_max<<" and "<<l_min<<endl; error=true;}	
	if (h_max<h_min) {cout<<"error! maximum h value must be larger than the minimal value. current values = "<<h_max<<" and "<<h_min<<endl; error=true;}
	if (s_max<s_min) {cout<<"error! maximum s value must be larger than the minimal value. current values = "<<s_max<<" and "<<s_min<<endl; error=true;}
	if (rho_max<rho_min) {cout<<"error! maximum rho value must be larger than the minimal value. current values = "<<rho_max<<" and "<<rho_min<<endl; error=true;}
	if (logp_max<logp_min) {cout<<"error! maximum logp value must be larger than the minimal value. current values = "<<logp_max/log(10)<<" and "<<logp_min/log(10)<<endl; error=true;}
	
	if (h_max>1) {cout<<"error! h values must be bewtween 0 and 1. current values = "<<h_max<<" and "<<h_min<<endl; error=true;}
	if (rho_max>1) {cout<<"error! rho values must be between 0 and 1. current values = "<<rho_max<<" and "<<rho_min<<endl; error=true;}
	if (logp_max>0) {cout<<"error! maximum logp value must be smaller than 0. current values = "<<logp_max/log(10)<<" and "<<logp_min/log(10)<<endl; error=true;}
	if (l_max<l_min) {cout<<"error! maximum lambda value must be larger than the minimal value. current values = "<<l_max<<" and "<<l_min<<endl; error=true;}
		
	if (h_scale>1.0) {cout<<"error! hscale value must be between 0 and 1. current value = "<<h_scale<<endl; error=true;}
	if (rho_scale>1.0) {cout<<"error! rscale value must be between 0 and 1. current value = "<<rho_scale<<endl; error=true;}
	if (logp_scale>1.0) {cout<<"error! pscale value must be between 0 and 1. current value = "<<logp_scale<<endl; error=true;}

	if (rho_max==1 && rho_min==1 && a_mode==12) {cout<<"error! ridge regression does not support a rho parameter. current values = "<<rho_max<<" and "<<rho_min<<endl; error=true;}
		
	//check p_column, and (no need to) sort p_column into ascending order
	if (p_column.size()==0) {
		p_column.push_back(1);
	} else {
		for (size_t i=0; i<p_column.size(); i++) {
			for (size_t j=0; j<i; j++) {
				if (p_column[i]==p_column[j]) {cout<<"error! identical phenotype columns: "<<p_column[i]<<endl; error=true;}
			}
		}
	}
	
	//sort (p_column.begin(), p_column.end() );
	n_ph=p_column.size();
	
		
	
	//only lmm option (and one prediction option) can deal with multiple phenotypes
	//and no gene expression files
	if (n_ph>1 && a_mode!=1 && a_mode!=2 && a_mode!=3 && a_mode!=4 && a_mode!=43) {
		cout<<"error! the current analysis mode "<<a_mode<<" can not deal with multiple phenotypes."<<endl; error=true;
	}
	if (n_ph>1 && !file_gene.empty() ) {
		cout<<"error! multiple phenotype analysis option not allowed with gene expression files. "<<endl; error=true;
	}
	
	if (p_nr>1) {
		cout<<"error! pnr value must be between 0 and 1. current value = "<<p_nr<<endl; error=true;
	}
	
	//check est_column
	if (est_column.size()==0) {
		if (file_ebv.empty()) {
			est_column.push_back(2);
			est_column.push_back(5);
			est_column.push_back(6);
			est_column.push_back(7);
		} else {
			est_column.push_back(2);
			est_column.push_back(0);
			est_column.push_back(6);
			est_column.push_back(7);
		}
	}
	
	if (est_column.size()!=4) {cout<<"error! -en not followed by four numbers. current number = "<<est_column.size()<<endl; error=true;}	
	if (est_column[0]==0) {cout<<"error! -en rs column can not be zero. current number = "<<est_column.size()<<endl; error=true;}
	
	//check if files are compatible with each other, and if files exist
	if (!file_bfile.empty()) {
		str=file_bfile+".bim";
		if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open .bim file: "<<str<<endl; error=true;}
		str=file_bfile+".bed";
		if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open .bed file: "<<str<<endl; error=true;}
		str=file_bfile+".fam";
		if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open .fam file: "<<str<<endl; error=true;}			
	}
	
	if ((!file_geno.empty() || !file_gene.empty()) ) {
		str=file_pheno;
		if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open phenotype file: "<<str<<endl; error=true;}
	}	
	
	str=file_geno;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open mean genotype file: "<<str<<endl; error=true;}
	
	str=file_gene;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open gene expression file: "<<str<<endl; error=true;}
	
	size_t flag=0;
	if (!file_bfile.empty()) {flag++;}
	if (!file_geno.empty()) {flag++;}
	if (!file_gene.empty()) {flag++;}
	
	if (flag!=1 && a_mode!=43 && a_mode!=5 && a_mode!=61) {
		cout<<"error! either plink binary files, or bimbam mean genotype files, or gene expression files are required."<<endl; error=true;
	}
	
	if (file_pheno.empty() && (a_mode==43 || a_mode==5 || a_mode==61) ) {
		cout<<"error! phenotype file is required."<<endl; error=true;
	}
	
	if (!file_epm.empty() && file_bfile.empty() && file_geno.empty() ) {cout<<"error! estimated parameter file also requires genotype file."<<endl; error=true;}
	if (!file_ebv.empty() && file_kin.empty()) {cout<<"error! estimated breeding value file also requires relatedness file."<<endl; error=true;}
	
	if (!file_log.empty() && pheno_mean!=0) {cout<<"error! either log file or mu value can be provide."<<endl; error=true;}
	
	str=file_snps;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open snps file: "<<str<<endl; error=true;}
	
	str=file_log;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open log file: "<<str<<endl; error=true;}
	
	str=file_anno;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open annotation file: "<<str<<endl; error=true;}

	str=file_kin;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open relatedness matrix file: "<<str<<endl; error=true;}

	str=file_mk;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open relatedness matrix file: "<<str<<endl; error=true;}
	
	str=file_cvt;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open covariates file: "<<str<<endl; error=true;}
	
	str=file_epm;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open estimated parameter file: "<<str<<endl; error=true;}
	
	str=file_ebv;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open estimated breeding value file: "<<str<<endl; error=true;}
	
	str=file_read;
	if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open total read file: "<<str<<endl; error=true;}
		
	//check if files are compatible with analysis mode
	if (k_mode==2 && !file_geno.empty() ) {cout<<"error! use \"-km 1\" when using bimbam mean genotype file. "<<endl; error=true;}
	
	if ((a_mode==1 || a_mode==2 || a_mode==3 || a_mode==4 || a_mode==5 || a_mode==31) && (file_kin.empty() && (file_ku.empty()||file_kd.empty())) )  {cout<<"error! missing relatedness file. "<<endl;  error=true;}

	if (a_mode==61 && (file_kin.empty() && (file_ku.empty()||file_kd.empty()) && file_mk.empty() ) )  {cout<<"error! missing relatedness file. "<<endl;  error=true;}

	if ((a_mode==43) && file_kin.empty())  {cout<<"error! missing relatedness file. -predict option requires -k option to provide a relatedness file."<<endl;  error=true;}
	
	if ((a_mode==11 || a_mode==12 || a_mode==13) && !file_cvt.empty() ) {cout<<"error! -bslmm option does not support covariates files."<<endl; error=true;}
		
	if (a_mode==41 || a_mode==42) {
		if (!file_cvt.empty() ) {cout<<"error! -predict option does not support covariates files."<<endl; error=true;}	
		if (file_epm.empty() ) {cout<<"error! -predict option requires estimated parameter files."<<endl; error=true;}		
	}

	return;
}


		


void PARAM::CheckData (void) {
	if ((file_cvt).empty() || (indicator_cvt).size()==0) {
		n_cvt=1;
	}
	if ( (indicator_cvt).size()!=0 && (indicator_cvt).size()!=(indicator_idv).size()) {
		error=true;
		cout<<"error! number of rows in the covariates file do not match the number of individuals. "<<endl;
		return;
	}
	
	if ( (indicator_read).size()!=0 && (indicator_read).size()!=(indicator_idv).size()) {
		error=true;
		cout<<"error! number of rows in the total read file do not match the number of individuals. "<<endl;
		return;
	}

	//calculate ni_total and ni_test, and set indicator_idv to 0 whenever indicator_cvt=0
	//and calculate np_obs and np_miss
	ni_total=(indicator_idv).size();
	
	ni_test=0; 
	for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
		if (indicator_idv[i]==0) {continue;}
		ni_test++;
	}
	
	ni_cvt=0;
	for (size_t i=0; i<indicator_cvt.size(); i++) {
		if (indicator_cvt[i]==0) {continue;}
		ni_cvt++;
	}

	np_obs=0; np_miss=0;
	for (size_t i=0; i<indicator_pheno.size(); i++) {
		if (indicator_cvt.size()!=0) {
			if (indicator_cvt[i]==0) {continue;}
		}
		
		for (size_t j=0; j<indicator_pheno[i].size(); j++) {					
			if (indicator_pheno[i][j]==0) {
				np_miss++;
			} else {
				np_obs++;
			}
		}
	}

	/*
	if ((indicator_cvt).size()!=0) {
		ni_test=0; 
		for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
			indicator_idv[i]*=indicator_cvt[i];
			ni_test+=indicator_idv[i];
		}
	}	
	
	if ((indicator_read).size()!=0) {
		ni_test=0; 
		for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
			indicator_idv[i]*=indicator_read[i];
			ni_test+=indicator_idv[i];
		}
	}
	*/
	if (ni_test==0) {
		error=true;
		cout<<"error! number of analyzed individuals equals 0. "<<endl;
		return;
	}
	
	if (a_mode==43) {
		if (ni_cvt==ni_test) {
			error=true;
			cout<<"error! no individual has missing phenotypes."<<endl; 
			return;
		}
		if ((np_obs+np_miss)!=(ni_cvt*n_ph)) {
			error=true;
			//cout<<ni_cvt<<"\t"<<ni_test<<"\t"<<ni_total<<"\t"<<np_obs<<"\t"<<np_miss<<"\t"<<indicator_cvt.size()<<endl;
			cout<<"error! number of phenotypes do not match the summation of missing and observed phenotypes."<<endl; 
			return;
		}
	}

	//output some information
	cout<<"## number of total individuals = "<<ni_total<<endl;
	if (a_mode==43) {
		cout<<"## number of analyzed individuals = "<<ni_cvt<<endl;
		cout<<"## number of individuals with full phenotypes = "<<ni_test<<endl;
	} else {
		cout<<"## number of analyzed individuals = "<<ni_test<<endl;
	}
	cout<<"## number of covariates = "<<n_cvt<<endl;
	cout<<"## number of phenotypes = "<<n_ph<<endl;
	if (a_mode==43) {
		cout<<"## number of observed data = "<<np_obs<<endl;
		cout<<"## number of missing data = "<<np_miss<<endl;
	}
	if (!file_gene.empty()) {		
		cout<<"## number of total genes = "<<ng_total<<endl;
	} else if (file_epm.empty() && a_mode!=43 && a_mode!=5) {
		cout<<"## number of total SNPs = "<<ns_total<<endl;	
		cout<<"## number of analyzed SNPs = "<<ns_test<<endl;
	} else {}
	
	//set d_pace to 1000 for gene expression
	if (!file_gene.empty() && d_pace==100000) {
		d_pace=1000;
	}
	
	//for case-control studies, count #cases and #controls
	int flag_cc=0;
	if (a_mode==13) {	
		ni_case=0;
		ni_control=0;
		for (size_t i=0; i<indicator_idv.size(); i++) {
			if (indicator_idv[i]==0) {continue;}
		
			if (pheno[i][0]==0) {ni_control++;}
			else if (pheno[i][0]==1) {ni_case++;}
			else {flag_cc=1;}
		}
		cout<<"## number of cases = "<<ni_case<<endl;	
		cout<<"## number of controls = "<<ni_control<<endl;	
	}	
	
	if (flag_cc==1) {cout<<"Unexpected non-binary phenotypes for case/control analysis. Use default (BSLMM) analysis instead."<<endl; a_mode=11;}
	
	//set parameters for BSLMM
	//and check for predict
	if (a_mode==11 || a_mode==12 || a_mode==13) {
		if (a_mode==11) {n_mh=1;}	
		if (logp_min==0) {logp_min=-1.0*log((double)ns_test);}
	
		if (h_scale==-1) {h_scale=min(1.0, 10.0/sqrt((double)ni_test) );}
		if (rho_scale==-1) {rho_scale=min(1.0, 10.0/sqrt((double)ni_test) );}
		if (logp_scale==-1) {logp_scale=min(1.0, 5.0/sqrt((double)ni_test) );}
		
		if (h_min==-1) {h_min=0.0;}
		if (h_max==-1) {h_max=1.0;}
		
		if (s_max>ns_test) {s_max=ns_test; cout<<"s_max is re-set to the number of analyzed SNPs."<<endl;}
		if (s_max<s_min) {cout<<"error! maximum s value must be larger than the minimal value. current values = "<<s_max<<" and "<<s_min<<endl; error=true;}
	} else if (a_mode==41 || a_mode==42) {		
		if (indicator_bv.size()!=0) {
			if (indicator_idv.size()!=indicator_bv.size()) {
				cout<<"error! number of rows in the phenotype file does not match that in the estimated breeding value file: "<<indicator_idv.size()<<"\t"<<indicator_bv.size()<<endl;
				error=true;
			} else {
				size_t flag_bv=0;
				for (size_t i=0; i<(indicator_bv).size(); ++i) {
					if (indicator_idv[i]!=indicator_bv[i]) {flag_bv++;}
				}
				if (flag_bv!=0) {
					cout<<"error! individuals with missing value in the phenotype file does not match that in the estimated breeding value file: "<<flag_bv<<endl;
					error=true;
				}
			}
		}
	}

	//file_mk needs to contain more than one line
	if (n_vc==1 && !file_mk.empty()) {cout<<"error! -mk file should contain more than one line."<<endl; error=true;}
	
	return;
}


void PARAM::PrintSummary () 
{
	if (n_ph==1) {
		cout<<"pve estimate ="<<pve_null<<endl;
		cout<<"se(pve) ="<<pve_se_null<<endl;
	} else {
		
	}
	return;
}



void PARAM::ReadGenotypes (gsl_matrix *UtX, gsl_matrix *K, const bool calc_K) {
	string file_str;
	
	if (!file_bfile.empty()) {
		file_str=file_bfile+".bed";
		if (ReadFile_bed (file_str, indicator_idv, indicator_snp, UtX, K, calc_K)==false) {error=true;}
	}
	else {
		if (ReadFile_geno (file_geno, indicator_idv, indicator_snp, UtX, K, calc_K)==false) {error=true;}
	}
	
	return;
}
		



void PARAM::CalcKin (gsl_matrix *matrix_kin)  {
	string file_str;
	
	gsl_matrix_set_zero (matrix_kin);
	
	if (!file_bfile.empty() ) {		
		file_str=file_bfile+".bed";
		if (PlinkKin (file_str, indicator_snp, a_mode-20, d_pace, matrix_kin)==false) {error=true;}
	}
	else {
		file_str=file_geno;
		if (BimbamKin (file_str, indicator_snp, a_mode-20, d_pace, matrix_kin)==false) {error=true;}
	}
	
	return;
}
		




void PARAM::WriteMatrix (const gsl_matrix *matrix_U, const string suffix) 
{
	string file_str;
	file_str=path_out+"/"+file_out;
	file_str+=".";
	file_str+=suffix;
	file_str+=".txt";	
	
	ofstream outfile (file_str.c_str(), ofstream::out);
	if (!outfile) {cout<<"error writing file: "<<file_str.c_str()<<endl; return;}
	
	outfile.precision(10);
	
	for (size_t i=0; i<matrix_U->size1; ++i) {
		for (size_t j=0; j<matrix_U->size2; ++j) {
			outfile<<gsl_matrix_get (matrix_U, i, j)<<"\t";
		}
		outfile<<endl;
	}
	
	outfile.close();
	outfile.clear();
	return;
}


void PARAM::WriteVector (const gsl_vector *vector_D, const string suffix) 
{
	string file_str;
	file_str=path_out+"/"+file_out;
	file_str+=".";
	file_str+=suffix;
	file_str+=".txt";
	
	ofstream outfile (file_str.c_str(), ofstream::out);
	if (!outfile) {cout<<"error writing file: "<<file_str.c_str()<<endl; return;}
	
	outfile.precision(10);
	
	for (size_t i=0; i<vector_D->size; ++i) {
		outfile<<gsl_vector_get (vector_D, i)<<endl;
	}
	
	outfile.close();
	outfile.clear();
	return;
}


void PARAM::CheckCvt () 
{
	if (indicator_cvt.size()==0) {return;}
		
	size_t ci_test=0;
	
	gsl_matrix *W=gsl_matrix_alloc (ni_test, n_cvt);
	
	for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) {
		if (indicator_idv[i]==0 || indicator_cvt[i]==0) {continue;}
		for (size_t j=0; j<n_cvt; ++j) {
			gsl_matrix_set (W, ci_test, j, (cvt)[i][j]);
		}
		ci_test++;
	}

	size_t flag_ipt=0;
	double v_min, v_max;
	set<size_t> set_remove;
	
	//check if any columns is an intercept
	for (size_t i=0; i<W->size2; i++) {
		gsl_vector_view w_col=gsl_matrix_column (W, i);
		gsl_vector_minmax (&w_col.vector, &v_min, &v_max);
		if (v_min==v_max) {flag_ipt=1; set_remove.insert (i);}
	}
	
	//add an intecept term if needed
	if (n_cvt==set_remove.size()) {
		indicator_cvt.clear();
		n_cvt=1;
	} else if (flag_ipt==0) {
		cout<<"no intecept term is found in the cvt file. a column of 1s is added."<<endl;
		for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) {
			if (indicator_idv[i]==0 || indicator_cvt[i]==0) {continue;}
			cvt[i].push_back(1.0);
		}
		
		n_cvt++;
	} else {}	
	
	gsl_matrix_free(W);
	
	return;
}


//post-process phentoypes, covariates
void PARAM::ProcessCvtPhen ()
{	
	//convert indicator_pheno to indicator_idv
	int k=1;
	indicator_idv.clear();
	for (size_t i=0; i<indicator_pheno.size(); i++) {
		k=1;
		for (size_t j=0; j<indicator_pheno[i].size(); j++) {
			if (indicator_pheno[i][j]==0) {k=0;}
		}
		indicator_idv.push_back(k);
	}
	
	//remove individuals with missing covariates
	if ((indicator_cvt).size()!=0) {
		for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
			indicator_idv[i]*=indicator_cvt[i];
		}
	}
	
	//obtain ni_test
	ni_test=0; 
	for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
		if (indicator_idv[i]==0) {continue;}
		ni_test++;
	}
	
	if (ni_test==0) {
		error=true;
		cout<<"error! number of analyzed individuals equals 0. "<<endl;
		return;
	}
	
	//check covariates to see if they are correlated with each other, and to see if the intercept term is included
	//after getting ni_test
	//add or remove covariates
	if (indicator_cvt.size()!=0) {
		CheckCvt();
	} else {
		vector<double> cvt_row;
		cvt_row.push_back(1);
		
		for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) {
			indicator_cvt.push_back(1);
			
			cvt.push_back(cvt_row);
		}
	}
	 
	return;
}




void PARAM::CopyCvt (gsl_matrix *W) 
{
	size_t ci_test=0;
	
	for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) {
		if (indicator_idv[i]==0 || indicator_cvt[i]==0) {continue;}
		for (size_t j=0; j<n_cvt; ++j) {
			gsl_matrix_set (W, ci_test, j, (cvt)[i][j]);
		}
		ci_test++;
	}
	
	return;
}


//if flag=0, then use indicator_idv to load W and Y
//else, use indicator_cvt to load them
void PARAM::CopyCvtPhen (gsl_matrix *W, gsl_vector *y, size_t flag) 
{
	size_t ci_test=0;
	
	for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) {
		if (flag==0) {
			if (indicator_idv[i]==0) {continue;}
		} else {
			if (indicator_cvt[i]==0) {continue;}
		}
		
		gsl_vector_set (y, ci_test, (pheno)[i][0]);
		
		for (size_t j=0; j<n_cvt; ++j) {
			gsl_matrix_set (W, ci_test, j, (cvt)[i][j]);
		}
		ci_test++;
	}
	
	return;
}

//if flag=0, then use indicator_idv to load W and Y
//else, use indicator_cvt to load them
void PARAM::CopyCvtPhen (gsl_matrix *W, gsl_matrix *Y, size_t flag) 
{
	size_t ci_test=0;
	
	for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) {
		if (flag==0) {
			if (indicator_idv[i]==0) {continue;}
		} else {
			if (indicator_cvt[i]==0) {continue;}
		}		
		
		for (size_t j=0; j<n_ph; ++j) {
			gsl_matrix_set (Y, ci_test, j, (pheno)[i][j]);
		}
		for (size_t j=0; j<n_cvt; ++j) {
			gsl_matrix_set (W, ci_test, j, (cvt)[i][j]);
		}
		ci_test++;
	}
	
	return;
}





void PARAM::CopyRead (gsl_vector *log_N) 
{
	size_t ci_test=0;
	
	for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) {
		if (indicator_idv[i]==0) {continue;}
		gsl_vector_set (log_N, ci_test, log(vec_read[i]) );	
		ci_test++;
	}
	
	return;
}