version 0.95alpha

1 files changed, 849 insertions, 0 deletions

diff --git a/src/param.cpp b/src/param.cpp
new file mode 100644
index 0000000..7a89ff8
--- /dev/null
+++ b/src/param.cpp
@@ -0,0 +1,849 @@
+/*
+    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;
+}
+		
+		
+