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

    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 "gsl/gsl_vector.h"
#include "gsl/gsl_matrix.h"
#include "gsl/gsl_linalg.h"
#include "gsl/gsl_blas.h"
#include "gsl/gsl_eigen.h"
#include "gsl/gsl_cdf.h"

#include "lapack.h"  //for functions EigenDecomp

#ifdef FORCE_FLOAT
#include "io_float.h"   //for function ReadFile_kin
#include "gemma_float.h"
#include "bslmm_float.h"  //for BSLMM class
#include "lmm_float.h"  //for LMM class, and functions CalcLambda and CalcPve
#include "prdt_float.h"	//for PRDT class
#include "mathfunc_float.h"	//for a few functions
#else
#include "io.h"
#include "gemma.h"
#include "bslmm.h"
#include "lmm.h"
#include "prdt.h"
#include "mathfunc.h"
#endif


using namespace std;



GEMMA::GEMMA(void):	
version("0.93"), date("12/05/2012"), year("2011")
{}

void GEMMA::PrintHeader (void)
{
	cout<<endl;
	cout<<"*********************************************************"<<endl;
	cout<<"  Genome-wide Efficient Mixed Model Association (GEMMA) "<<endl;
	cout<<"  Version "<<version<<", "<<date<<"                              "<<endl;
	cout<<"  Visit                                                 "<<endl;
	cout<<"     http://stephenslab.uchicago.edu/software.html      "<<endl;
	cout<<"     http://home.uchicago.edu/~xz7/software.html        "<<endl;
	cout<<"  For Possible Updates                                  "<<endl;
	cout<<"  (C) "<<year<<" Xiang Zhou and Matthew Stephens              "<<endl;
	cout<<"  GNU General Public License                            "<<endl;
	cout<<"  For Help, Type ./gemma -h                             "<<endl;
	cout<<"*********************************************************"<<endl;
	cout<<endl;
	
	return;
}


void GEMMA::PrintLicense (void)
{
	cout<<endl;
	cout<<"The Software Is Distributed Under GNU General Public License, But May Also Require The Following Notifications."<<endl;
	cout<<endl;
	
	cout<<"Including Lapack Routines In The Software May Require The Following Notification:"<<endl;
	cout<<"Copyright (c) 1992-2010 The University of Tennessee and The University of Tennessee Research Foundation.  All rights reserved."<<endl;
	cout<<"Copyright (c) 2000-2010 The University of California Berkeley. All rights reserved."<<endl;
	cout<<"Copyright (c) 2006-2010 The University of Colorado Denver.  All rights reserved."<<endl;	
	cout<<endl;
	cout<<"Including Arpack Routines In The Software May Require The Following Notification:"<<endl;
	cout<<"Copyright (c) 1996-2008 Rice University."<<endl;
	cout<<"Developed by D.C. Sorensen, R.B. Lehoucq, C. Yang, and K. Maschhoff."<<endl;
	cout<<"All rights reserved."<<endl;	
	cout<<endl;
	
	cout<<"$COPYRIGHT$"<<endl;
	cout<<"Additional copyrights may follow"<<endl;
	cout<<"$HEADER$"<<endl;
	cout<<"Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:"<<endl;
	cout<<"- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer."<<endl;
	cout<<"- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer listed in this license in the documentation and/or other materials provided with the distribution."<<endl;
	cout<<"- Neither the name of the copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission."<<endl;
	cout<<"The copyright holders provide no reassurances that the source code provided does not infringe any patent, copyright, or any other "
		<<"intellectual property rights of third parties.  The copyright holders disclaim any liability to any recipient for claims brought against "
		<<"recipient by any third party for infringement of that parties intellectual property rights. "<<endl;
	cout<<"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT "
		<<"LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT "
		<<"OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT "
		<<"LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY "
		<<"THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE "
		<<"OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."<<endl;
	cout<<endl;
	
	
	
	return;
}



void GEMMA::PrintHelp(size_t option)
{
	if (option==0) {
		cout<<endl; 
		cout<<" GEMMA version "<<version<<", released on "<<date<<endl;
		cout<<" implemented by X. Zhou"<<endl; 
		cout<<endl;
		cout<<" type ./gemma -h [num] for detailed helps"<<endl;
		cout<<" options: " << endl;
		cout<<" 1: quick guide"<<endl;
		cout<<" 2: file I/O related"<<endl;
		cout<<" 3: SNP QC"<<endl;
		cout<<" 4: calculate relatedness matrix"<<endl;
		cout<<" 5: fit linear mixed model"<<endl;
		cout<<" 6: fit Bayesian sparse linear mixed model"<<endl;
		cout<<" 7: obtain predicted values"<<endl;
		cout<<" 8: note"<<endl;
		cout<<endl;
	}	
	
	if (option==1) {
		cout<<" QUICK GUIDE" << endl;
		cout<<" to generate a relatedness matrix: "<<endl;
		cout<<"         ./gemma -bfile [prefix] -gk [num] -o [prefix]"<<endl;
		cout<<"         ./gemma -g [filename] -p [filename] -gk [num] -o [prefix]"<<endl;
		cout<<" to fit a linear mixed model: "<<endl;
		cout<<"         ./gemma -bfile [prefix] -k [filename] -lmm [num] -o [prefix]"<<endl;
		cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -o [prefix]"<<endl;	
		cout<<" to fit a Bayesian sparse linear mixed model: "<<endl;
		cout<<"         ./gemma -bfile [prefix] -bslmm [num] -o [prefix]"<<endl;
		cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -bslmm [num] -o [prefix]"<<endl;
		cout<<" to obtain predicted values: "<<endl;
		cout<<"         ./gemma -bfile [prefix] -epm [filename] -emu [filename] -ebv [filename] -k [filename] -predict [num] -o [prefix]"<<endl;
		cout<<"         ./gemma -g [filename] -p [filename] -epm [filename] -emu [filename] -ebv [filename] -k [filename] -predict [num] -o [prefix]"<<endl;
		cout<<endl;
	}
	
	if (option==2) {
		cout<<" FILE I/O RELATED OPTIONS" << endl;
		cout<<" -bfile    [prefix]       "<<" specify input PLINK binary ped file prefix."<<endl;	
		cout<<"          requires: *.fam, *.bim and *.bed files"<<endl;	
		cout<<"          missing value: -9"<<endl;
		cout<<" -g        [filename]     "<<" specify input BIMBAM mean genotype file name"<<endl;
		cout<<"          format: rs#1, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl;	
		cout<<"                  rs#2, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl;	
		cout<<"                  ..."<<endl;	
		cout<<"          missing value: NA"<<endl;	
		cout<<" -p        [filename]     "<<" specify input BIMBAM phenotype file name"<<endl;
		cout<<"          format: phenotype for individual 1"<<endl;	
		cout<<"                  phenotype for individual 2"<<endl;	
		cout<<"                  ..."<<endl;
		cout<<"          missing value: NA"<<endl;	
		cout<<" -a        [filename]     "<<" specify input BIMBAM SNP annotation file name (optional)"<<endl;	
		cout<<"          format: rs#1, base_position, chr_number"<<endl;	
		cout<<"                  rs#2, base_position, chr_number"<<endl;	
		cout<<"                  ..."<<endl;
		cout<<" -k        [filename]     "<<" specify input kinship/relatedness matrix file name"<<endl;	
		cout<<" -c        [filename]     "<<" specify input covariates file name (optional)"<<endl;	
		cout<<"          format: covariate 1 for individual 1, ... , covariate c for individual 1"<<endl;	
		cout<<"                  covariate 1 for individual 2, ... , covariate c for individual 2"<<endl;	
		cout<<"                  ..."<<endl;
		cout<<"          missing value: NA"<<endl;	
		cout<<"          note: the intercept (a column of 1s) may need to be included"<<endl;
		cout<<" -epm      [filename]     "<<" specify input estimated parameter file name"<<endl;
		cout<<" -en [n1] [n2] [n3] [n4]  "<<" specify values for the input estimated parameter file (with a header)"<<endl;
		cout<<"          options: n1: rs column number"<<endl;
		cout<<"                   n2: estimated alpha column number (0 to ignore)"<<endl;
		cout<<"                   n3: estimated beta column number (0 to ignore)"<<endl;
		cout<<"                   n4: estimated gamma column number (0 to ignore)"<<endl;
		cout<<"          default: 2 4 5 6 if -ebv is not specified; 2 0 5 6 if -ebv is specified"<<endl;
		cout<<" -ebv      [filename]     "<<" specify input estimated random effect (breeding value) file name"<<endl;
		cout<<"          format: value for individual 1"<<endl;	
		cout<<"                  value for individual 2"<<endl;	
		cout<<"                  ..."<<endl;
		cout<<"          missing value: NA"<<endl;	
		cout<<" -emu      [filename]     "<<" specify input log file name containing estimated mean"<<endl;
		cout<<" -mu       [num]          "<<" specify input estimated mean value"<<endl;
		cout<<" -gene     [filename]     "<<" specify input gene expression file name"<<endl;
		cout<<"          format: header"<<endl;	
		cout<<"                  gene1, count for individual 1, count for individual 2, ..."<<endl;	
		cout<<"                  gene2, count for individual 1, count for individual 2, ..."<<endl;	
		cout<<"                  ..."<<endl;
		cout<<"          missing value: not allowed"<<endl;	
		cout<<" -r        [filename]     "<<" specify input total read count file name"<<endl;
		cout<<"          format: total read count for individual 1"<<endl;	
		cout<<"                  total read count for individual 2"<<endl;	
		cout<<"                  ..."<<endl;
		cout<<"          missing value: NA"<<endl;	
		cout<<" -snps     [filename]     "<<" specify input snps file name to only analyze a certain set of snps"<<endl;
		cout<<"          format: rs#1"<<endl;	
		cout<<"                  rs#2"<<endl;	
		cout<<"                  ..."<<endl;
		cout<<"          missing value: NA"<<endl;	
		cout<<" -km       [num]          "<<" specify input kinship/relatedness file type (default 1)."<<endl;
		cout<<"          options: 1: \"n by n matrix\" format"<<endl;
		cout<<"                   2: \"id  id  value\" format"<<endl;
		cout<<" -n        [num]          "<<" specify phenotype column in the phenotype/*.fam file (optional; default 1)"<<endl;	
		cout<<" -pace     [num]          "<<" specify terminal display update pace (default 100000 SNPs or 100000 iterations)."<<endl;
		cout<<" -o        [prefix]       "<<" specify output file prefix (default \"result\")"<<endl;  
		cout<<"          output: prefix.cXX.txt or prefix.sXX.txt from kinship/relatedness matrix estimation"<<endl;	
		cout<<"          output: prefix.assoc.txt and prefix.log.txt form association tests"<<endl;	
		cout<<endl;
	}
	
	if (option==3) {
		cout<<" SNP QC OPTIONS" << endl;
		cout<<" -miss     [num]          "<<" specify missingness threshold (default 0.05)" << endl; 
		cout<<" -maf      [num]          "<<" specify minor allele frequency threshold (default 0.01)" << endl; 
		cout<<" -notsnp                  "<<" minor allele frequency cutoff is not used" << endl; 
		cout<<endl;
	}
	
	if (option==4) {
		cout<<" RELATEDNESS MATRIX CALCULATION OPTIONS" << endl;
		cout<<" -gk       [num]          "<<" specify which type of kinship/relatedness matrix to generate (default 1)" << endl; 
		cout<<"          options: 1: centered XX^T/p"<<endl;
		cout<<"                   2: standardized XX^T/p"<<endl;
		cout<<"          note: non-polymorphic SNPs are excluded "<<endl;
		cout<<endl;
	}
	
	if (option==5) {
		cout<<" LINEAR MIXED MODEL OPTIONS" << endl;		
		cout<<" -lmm       [num]         "<<" specify analysis options (default 1)."<<endl;
		cout<<"          options: 1: Wald test"<<endl;
		cout<<"                   2: Likelihood ratio test"<<endl;
		cout<<"                   3: Score test"<<endl;
		cout<<"                   4: 1-3"<<endl;
		cout<<" -lmin     [num]          "<<" specify minimal value for lambda (default 1e-5)" << endl; 
		cout<<" -lmax     [num]          "<<" specify maximum value for lambda (default 1e+5)" << endl; 
		cout<<" -region   [num]          "<<" specify the number of regions used to evaluate lambda (default 10)" << endl; 
		cout<<endl;
	}
	
	if (option==6) {
		cout<<" MULTI-LOCUS ANALYSIS OPTIONS" << endl;
		cout<<" -bslmm	  [num]			 "<<" specify analysis options (default 1)."<<endl;
		cout<<"          options: 1: BSLMM"<<endl;	
		cout<<"                   2: standard ridge regression/GBLUP (no mcmc)"<<endl;	
		cout<<"                   3: probit BSLMM (requires 0/1 phenotypes)"<<endl;			
		
		cout<<"   MCMC OPTIONS" << endl;
		cout<<"   Prior" << endl;	
		cout<<" -hmin     [num]          "<<" specify minimum value for h (default 0)" << endl; 
		cout<<" -hmax     [num]          "<<" specify maximum value for h (default 1)" << endl; 
		cout<<" -rmin     [num]          "<<" specify minimum value for rho (default 0)" << endl; 
		cout<<" -rmax     [num]          "<<" specify maximum value for rho (default 1)" << endl; 
		cout<<" -pmin     [num]          "<<" specify minimum value for log10(pi) (default log10(1/p), where p is the number of analyzed SNPs )" << endl; 
		cout<<" -pmax     [num]          "<<" specify maximum value for log10(pi) (default log10(1) )" << endl; 	
		cout<<" -smin     [num]          "<<" specify minimum value for |gamma| (default 0)" << endl; 
		cout<<" -smax     [num]          "<<" specify maximum value for |gamma| (default 300)" << endl; 
		
		cout<<"   Proposal" << endl;
		cout<<" -gmean    [num]          "<<" specify the mean for the geometric distribution (default: 2000)" << endl; 
		cout<<" -hscale   [num]          "<<" specify the step size scale for the proposal distribution of h (value between 0 and 1, default min(10/sqrt(n),1) )" << endl; 
		cout<<" -rscale   [num]          "<<" specify the step size scale for the proposal distribution of rho (value between 0 and 1, default min(10/sqrt(n),1) )" << endl; 
		cout<<" -pscale   [num]          "<<" specify the step size scale for the proposal distribution of log10(pi) (value between 0 and 1, default min(5/sqrt(n),1) )" << endl; 
		
		cout<<"   Others" << endl;
		cout<<" -w        [num]          "<<" specify burn-in steps (default 100,000)" << endl; 
		cout<<" -s        [num]          "<<" specify sampling steps (default 1,000,000)" << endl; 
		cout<<" -rpace    [num]          "<<" specify recording pace, record one state in every [num] steps (default 10)" << endl; 	
		cout<<" -wpace    [num]          "<<" specify writing pace, write values down in every [num] recorded steps (default 1000)" << endl; 	
		cout<<" -seed     [num]          "<<" specify random seed (a random seed is generated by default)" << endl; 	
		cout<<" -mh       [num]          "<<" specify number of MH steps in each iteration (default 10)" << endl; 
		cout<<"          requires: 0/1 phenotypes and -bslmm 3 option"<<endl;	
		cout<<endl;
	}
	
	if (option==7) {
		cout<<" PREDICTION OPTIONS" << endl;
		cout<<" -predict  [num]			 "<<" specify prediction options (default 1)."<<endl;
		cout<<"          options: 1: predict for individuals with missing phenotypes"<<endl;	
		cout<<"                   2: predict for individuals with missing phenotypes, and convert the predicted values to probability scale. Use only for files fitted with -bslmm 3 option"<<endl;	
		cout<<endl;
	}
	
	if (option==8) {
		cout<<" NOTE"<<endl;
		cout<<" 1. Only individuals with non-missing phenotoypes and covariates will be analyzed."<<endl;
		cout<<" 2. Missing genotoypes will be repalced with the mean genotype of that SNP."<<endl;
		cout<<" 3. For lmm analysis, memory should be large enough to hold the relatedness matrix and to perform eigen decomposition."<<endl;
		cout<<" 4. For bslmm analysis, in addition to 3, memory should be large enough to hold the whole genotype matrix."<<endl;
		cout<<endl;
	}
	
	return;
}



void GEMMA::Assign(int argc, char ** argv, PARAM &cPar)
{
	string str;
	
	for(int i = 1; i < argc; i++) {		
		if (strcmp(argv[i], "-bfile")==0 || strcmp(argv[i], "--bfile")==0 || strcmp(argv[i], "-b")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_bfile=str;
		}
		else if (strcmp(argv[i], "-g")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_geno=str;
		}
		else if (strcmp(argv[i], "-p")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_pheno=str;
		}
		else if (strcmp(argv[i], "-a")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_anno=str;
		}
		else if (strcmp(argv[i], "-k")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_kin=str;
		}
		else if (strcmp(argv[i], "-c")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_cvt=str;
		}
		else if (strcmp(argv[i], "-epm")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_epm=str;
		}
		else if (strcmp(argv[i], "-en")==0) {			
			while (argv[i+1] != NULL && argv[i+1][0] != '-') {
				++i;
				str.clear();
				str.assign(argv[i]);
				cPar.est_column.push_back(atoi(str.c_str()));
			}
		}
		else if (strcmp(argv[i], "-ebv")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_ebv=str;
		}
		else if (strcmp(argv[i], "-emu")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_log=str;
		}
		else if (strcmp(argv[i], "-mu")==0) {
			if(argv[i+1] == NULL) {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.pheno_mean=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-gene")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_gene=str;
		}
		else if (strcmp(argv[i], "-r")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_read=str;
		}
		else if (strcmp(argv[i], "-snps")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_snps=str;
		}
		else if (strcmp(argv[i], "-km")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.k_mode=atoi(str.c_str());
		}		
		else if (strcmp(argv[i], "-n")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.p_column=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-pace")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.d_pace=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-o")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.file_out=str;
		}		
		else if (strcmp(argv[i], "-miss")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.miss_level=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-maf")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.maf_level=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-notsnp")==0) {
			cPar.maf_level=-1;
		}
		else if (strcmp(argv[i], "-gk")==0) {
			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -lmm -bslmm -predict options is allowed."<<endl; break;}
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=21; continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.a_mode=20+atoi(str.c_str());
		}	
		else if (strcmp(argv[i], "-fa")==0 || strcmp(argv[i], "-lmm")==0) {
			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -lmm -bslmm -predict options is allowed."<<endl; break;}
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=1; continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.a_mode=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-lmin")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.l_min=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-lmax")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.l_max=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-region")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.n_region=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-bslmm")==0) {
			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -lmm -bslmm -predict options is allowed."<<endl; break;}
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=11; continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.a_mode=10+atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-hmin")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.h_min=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-hmax")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.h_max=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-rmin")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.rho_min=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-rmax")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.rho_max=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-pmin")==0) {
			if(argv[i+1] == NULL) {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.logp_min=atof(str.c_str())*log(10.0);
		}
		else if (strcmp(argv[i], "-pmax")==0) {
			if(argv[i+1] == NULL) {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.logp_max=atof(str.c_str())*log(10.0);
		}
		else if (strcmp(argv[i], "-smin")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.s_min=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-smax")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.s_max=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-gmean")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.geo_mean=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-hscale")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.h_scale=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-rscale")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.rho_scale=atof(str.c_str());
		}
		else if (strcmp(argv[i], "-pscale")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.logp_scale=atof(str.c_str())*log(10.0);
		}
		else if (strcmp(argv[i], "-w")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.w_step=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-s")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.s_step=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-rpace")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.r_pace=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-wpace")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.w_pace=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-seed")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.randseed=atol(str.c_str());
		}
		else if (strcmp(argv[i], "-mh")==0) {
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.n_mh=atoi(str.c_str());
		}
		else if (strcmp(argv[i], "-predict")==0) {
			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -lmm -bslmm -predict options is allowed."<<endl; break;}
			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=41; continue;}
			++i;
			str.clear();
			str.assign(argv[i]);
			cPar.a_mode=40+atoi(str.c_str());
		}
		else {cout<<"error! unrecognized option: "<<argv[i]<<endl; cPar.error=true; continue;}
	}
	
	return;
}



void GEMMA::BatchRun (PARAM &cPar) 
{
	clock_t time_begin, time_start;
	time_begin=clock();	
		
	//Read Files
	cout<<"Reading Files ... "<<endl;
	cPar.ReadFiles();
	if (cPar.error==true) {cout<<"error! fail to read files. "<<endl; return;}
	cPar.CheckData();
	if (cPar.error==true) {cout<<"error! fail to check data. "<<endl; return;}
	
	//Prediction
	if (cPar.a_mode==41 || cPar.a_mode==42) {
		gsl_vector *y_prdt;
		
		y_prdt=gsl_vector_alloc (cPar.ni_total-cPar.ni_test);
		
		//set to zero
		gsl_vector_set_zero (y_prdt);
		
		PRDT cPRDT;
		cPRDT.CopyFromParam(cPar);
		
		//add breeding value if needed
		if (!cPar.file_kin.empty() && !cPar.file_ebv.empty()) {
			cout<<"Adding Breeding Values ... "<<endl;
			
			gsl_matrix *G=gsl_matrix_alloc (cPar.ni_total, cPar.ni_total);
			gsl_vector *u_hat=gsl_vector_alloc (cPar.ni_test);
			
			//read kinship matrix and set u_hat
			vector<int> indicator_all;
			size_t c_bv=0;
			for (size_t i=0; i<cPar.indicator_idv.size(); i++) {
				indicator_all.push_back(1);
				if (cPar.indicator_bv[i]==1) {gsl_vector_set(u_hat, c_bv, cPar.vec_bv[i]); c_bv++;}
			}
			
			ReadFile_kin (cPar.file_kin, indicator_all, cPar.mapID2num, cPar.k_mode, cPar.error, G);
			if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
			
			//read u			
			cPRDT.AddBV(G, u_hat, y_prdt);					
			
			gsl_matrix_free(G);
			gsl_vector_free(u_hat);
		}
		
		//add beta
		if (!cPar.file_bfile.empty()) {
			cPRDT.AnalyzePlink (y_prdt);
		}
		else {
			cPRDT.AnalyzeBimbam (y_prdt);
		}
		
		//add mu
		gsl_vector_add_constant(y_prdt, cPar.pheno_mean);
		
		//convert y to probability if needed
		if (cPar.a_mode==42) {
			double d;
			for (size_t i=0; i<y_prdt->size; i++) {
				d=gsl_vector_get(y_prdt, i);
				d=gsl_cdf_gaussian_P(d, 1.0);
				gsl_vector_set(y_prdt, i, d);
			}
		}
			
			
		cPRDT.CopyToParam(cPar);
		
		cPRDT.WriteFiles(y_prdt);
		
		gsl_vector_free(y_prdt);
	}
	
	//Generate Kinship matrix
	if (cPar.a_mode==21 || cPar.a_mode==22) {  
		cout<<"Calculating Relatedness Matrix ... "<<endl;
		
		gsl_matrix *G=gsl_matrix_alloc (cPar.ni_total, cPar.ni_total);
		
		time_start=clock();
		cPar.CalcKin (G);
		cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
		if (cPar.error==true) {cout<<"error! fail to calculate relatedness matrix. "<<endl; return;}
		
		cPar.WriteKin (G);
		gsl_matrix_free (G);
	}
	
	//LMM
	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4) {  //Fit LMM or BSLMM
		gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
		gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt);		
		gsl_matrix *G=gsl_matrix_alloc (y->size, y->size);
		gsl_matrix *U=gsl_matrix_alloc (y->size, y->size); 
		gsl_vector *eval=gsl_vector_alloc (y->size);
		gsl_matrix *UtW=gsl_matrix_alloc (y->size, W->size2);
		gsl_vector *Uty=gsl_vector_alloc (y->size);
		
		//set covariates matrix W and phenotype vector y		
		//an intercept should be included in W, 
		cPar.CopyCvtPhen (W, y);
				
		//read relatedness matrix G		
		ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
		if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
		
		//eigen-decomposition and calculate trace_G
		cout<<"Start Eigen-Decomposition..."<<endl;
		time_start=clock();
		cPar.trace_G=EigenDecomp (G, U, eval);
		cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);			
		
		//calculate UtW and Uty		
		CalcUtX (U, W, UtW);
		CalcUtX (U, y, Uty);
		
		//calculate REMLE/MLE estimate and pve
		CalcLambda ('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
		CalcLambda ('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
		CalcPve (eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null);
		
		cPar.PrintSummary();
		
		//Fit LMM
		LMM cLmm;
		cLmm.CopyFromParam(cPar);
		
		if (!cPar.file_gene.empty()) {		
			cLmm.AnalyzeGene (U, eval, UtW, Uty, W, y); //y is the predictor, not the phenotype
		} else if (!cPar.file_bfile.empty()) {
			cLmm.AnalyzePlink (U, eval, UtW, Uty, W, y);
		} else {
			cLmm.AnalyzeBimbam (U, eval, UtW, Uty, W, y);
		}	
		
		cLmm.WriteFiles();
		cLmm.CopyToParam(cPar);
				
		//release all matrices and vectors
		gsl_matrix_free (W);
		gsl_matrix_free (G);	
		gsl_matrix_free (U);
		gsl_matrix_free (UtW);
		gsl_vector_free (eval);
		gsl_vector_free (y);
		gsl_vector_free (Uty);
	} 
	
	
	//BSLMM
	if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
		gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
		gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt);		
		gsl_matrix *G=gsl_matrix_alloc (y->size, y->size);
		gsl_matrix *U=gsl_matrix_alloc (y->size, y->size); 
		gsl_vector *eval=gsl_vector_alloc (y->size);
		gsl_matrix *UtW=gsl_matrix_alloc (y->size, W->size2);
		gsl_vector *Uty=gsl_vector_alloc (y->size);
		gsl_matrix *UtX=gsl_matrix_alloc (y->size, cPar.ns_test);
		
		//set covariates matrix W and phenotype vector y		
		//an intercept should be included in W, 
		cPar.CopyCvtPhen (W, y);
		
		//y is centered
		cPar.pheno_mean=CenterVector(y);
		
		//read relatedness matrix G		
		if (!(cPar.file_kin).empty()) {		
			cPar.ReadGenotypes (UtX, G, false);
			
			//read relatedness matrix G		
			ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
			if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
			
			//center matrix G
			CenterMatrix (G);			
		} else {
			cPar.ReadGenotypes (UtX, G, true);
		}
		
		//eigen-decomposition and calculate trace_G
		cout<<"Start Eigen-Decomposition..."<<endl;
		time_start=clock();
		cPar.trace_G=EigenDecomp (G, U, eval);
		cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);			
		
		//calculate UtW and Uty		
		CalcUtX (U, W, UtW);
		CalcUtX (U, y, Uty);
		
		//calculate REMLE/MLE estimate and pve
		CalcLambda ('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
		CalcLambda ('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
		CalcPve (eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null);
		
		cPar.PrintSummary();		
				
		//Creat and calcualte UtX, use a large memory
		cout<<"Calculating UtX..."<<endl;
		time_start=clock();							
		CalcUtX (U, UtX);
		cPar.time_UtX=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
		
		//perform BSLMM analysis
		BSLMM cBslmm;
		cBslmm.CopyFromParam(cPar);
		time_start=clock();	
		if (cPar.a_mode==12) {  //ridge regression				
			cBslmm.RidgeR(U, UtX, Uty, eval, cPar.l_remle_null);
		} else {	//Run MCMC
			cBslmm.MCMC(U, UtX, Uty, eval, y);
		}
		cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
		cBslmm.CopyToParam(cPar);
		
		//release all matrices and vectors
		gsl_matrix_free (W);
		gsl_matrix_free (G);	
		gsl_matrix_free (U);
		gsl_matrix_free (UtW);
		gsl_vector_free (eval);
		gsl_vector_free (y);
		gsl_vector_free (Uty);
		gsl_matrix_free (UtX);
	} 
	
	
		
	cPar.time_total=(clock()-time_begin)/(double(CLOCKS_PER_SEC)*60.0);
	
	return;
}




void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) 
{
	string file_str;
	file_str="./output/"+cPar.file_out;
	file_str+=".log.txt";
	
	ofstream outfile (file_str.c_str(), ofstream::out);
	if (!outfile) {cout<<"error writing log file: "<<file_str.c_str()<<endl; return;}
	
	outfile<<"##"<<endl;
	outfile<<"## GEMMA Version = "<<version<<endl;
	
	outfile<<"##"<<endl;
	outfile<<"## Command Line Input = ";
	for(int i = 1; i < argc; i++) {	
		outfile<<argv[i]<<" ";
	}
	outfile<<endl;
	
	outfile<<"##"<<endl;
	outfile<<"## Summary Statistics:"<<endl;
	outfile<<"## number of total individuals = "<<cPar.ni_total<<endl;	
	outfile<<"## number of analyzed individuals = "<<cPar.ni_test<<endl;
	outfile<<"## number of covariates = "<<cPar.n_cvt<<endl;
	
	if (!(cPar.file_gene).empty()) {
		outfile<<"## number of total genes = "<<cPar.ng_total<<endl;
		outfile<<"## number of analyzed genes = "<<cPar.ng_test<<endl;		
	} else {		
		outfile<<"## number of total SNPs = "<<cPar.ns_total<<endl;	
		outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
	}
	
	if (cPar.a_mode==13) {
		outfile<<"## number of cases = "<<cPar.ni_case<<endl;
		outfile<<"## number of controls = "<<cPar.ni_control<<endl;
	}
	
	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
		outfile<<"## lambda REMLE estimate in the null (linear mixed) model = "<<cPar.l_remle_null<<endl;
		outfile<<"## lambda MLE estimate in the null (linear mixed) model = "<<cPar.l_mle_null<<endl;	
		outfile<<"## pve estimate in the null (linear mixed) model = "<<cPar.pve_null<<endl;
		outfile<<"## se(pve) in the null (linear mixed) model = "<<cPar.pve_se_null<<endl;	
	}
	if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
		outfile<<"## estimated mean = "<<cPar.pheno_mean<<endl;
	}
	
	if (cPar.a_mode==11 || cPar.a_mode==13) {	
		outfile<<"##"<<endl;
		outfile<<"## MCMC related:"<<endl;	
		outfile<<"## initial value of h = "<<cPar.cHyp_initial.h<<endl;
		outfile<<"## initial value of rho = "<<cPar.cHyp_initial.rho<<endl;
		outfile<<"## initial value of pi = "<<exp(cPar.cHyp_initial.logp)<<endl;
		outfile<<"## initial value of |gamma| = "<<cPar.cHyp_initial.n_gamma<<endl;
		outfile<<"## random seed = "<<cPar.randseed<<endl;
		outfile<<"## acceptance ratio = "<<(double)cPar.n_accept/(double)((cPar.w_step+cPar.s_step)*cPar.n_mh)<<endl;
	}
	
	outfile<<"##"<<endl;
	outfile<<"## Computation Time:"<<endl;
	outfile<<"## total computation time = "<<cPar.time_total<<" min "<<endl;
	outfile<<"## computation time break down: "<<endl;
	if (cPar.a_mode==21 || cPar.a_mode==22 || cPar.a_mode==11 || cPar.a_mode==13) {
		outfile<<"##      time on calculating relatedness matrix = "<<cPar.time_G<<" min "<<endl;
	}
	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
		outfile<<"##      time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl;
		outfile<<"##      time on calculating UtX = "<<cPar.time_UtX<<" min "<<endl;		
	}
	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4) {
		outfile<<"##      time on optimization = "<<cPar.time_opt<<" min "<<endl;
	}
	if (cPar.a_mode==11 || cPar.a_mode==13) {
		outfile<<"##      time on mcmc = "<<cPar.time_opt<<" min "<<endl;
		outfile<<"##      time on Omega = "<<cPar.time_Omega<<" min "<<endl;
	}
	if (cPar.a_mode==41 || cPar.a_mode==42) {
		outfile<<"##      time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl;
	}
	outfile<<"##"<<endl;
	
	outfile.close();
	outfile.clear();
	return;
}