Added new files shared by Xiang via email on May 4, 2017.

1 files changed, 1015 insertions, 0 deletions

diff --git a/src/bslmmdap.cpp b/src/bslmmdap.cpp
new file mode 100644
index 0000000..0bf0e7b
--- /dev/null
+++ b/src/bslmmdap.cpp
@@ -0,0 +1,1015 @@
+/*
+ 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 <sstream>
+
+#include <iomanip>
+#include <cmath>
+#include <iostream>
+#include <stdio.h>
+#include <stdlib.h>
+#include <ctime>
+#include <cstring>
+#include <algorithm>
+
+#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_randist.h"
+#include "gsl/gsl_cdf.h"
+#include "gsl/gsl_roots.h"
+
+
+
+#include "logistic.h"
+#include "lapack.h"
+
+#ifdef FORCE_FLOAT
+#include "param_float.h"
+#include "bslmmdap_float.h"
+#include "lmm_float.h"  //for class FUNC_PARAM and MatrixCalcLR
+#include "lm_float.h"
+#include "mathfunc_float.h"  //for function CenterVector
+#else
+#include "param.h"
+#include "bslmmdap.h"
+#include "lmm.h"
+#include "lm.h"
+#include "mathfunc.h"
+#endif
+
+using namespace std;
+
+
+
+
+void BSLMMDAP::CopyFromParam (PARAM &cPar)
+{
+	file_out=cPar.file_out;
+	path_out=cPar.path_out;
+
+	time_UtZ=0.0;
+	time_Omega=0.0;
+
+	h_min=cPar.h_min;
+	h_max=cPar.h_max;
+	h_ngrid=cPar.h_ngrid;
+	rho_min=cPar.rho_min;
+	rho_max=cPar.rho_max;
+	rho_ngrid=cPar.rho_ngrid;
+
+	if (h_min<=0) {h_min=0.01;}
+	if (h_max>=1) {h_max=0.99;}
+	if (rho_min<=0) {rho_min=0.01;}
+	if (rho_max>=1) {rho_max=0.99;}
+
+	trace_G=cPar.trace_G;
+
+	ni_total=cPar.ni_total;
+	ns_total=cPar.ns_total;
+	ni_test=cPar.ni_test;
+	ns_test=cPar.ns_test;
+
+	indicator_idv=cPar.indicator_idv;
+	indicator_snp=cPar.indicator_snp;
+	snpInfo=cPar.snpInfo;
+
+	return;
+}
+
+
+void BSLMMDAP::CopyToParam (PARAM &cPar)
+{
+	cPar.time_UtZ=time_UtZ;
+	cPar.time_Omega=time_Omega;
+
+	return;
+}
+
+
+
+//read hyp file
+void ReadFile_hyb (const string &file_hyp, vector<double> &vec_sa2, vector<double> &vec_sb2, vector<double> &vec_wab)
+{
+  vec_sa2.clear(); vec_sb2.clear(); vec_wab.clear();
+
+  igzstream infile (file_hyp.c_str(), igzstream::in);
+  if (!infile) {cout<<"error! fail to open hyp file: "<<file_hyp<<endl; return;}
+
+  string line;
+  char *ch_ptr;
+
+  getline(infile, line);
+
+  while (!safeGetline(infile, line).eof()) {
+    ch_ptr=strtok ((char *)line.c_str(), " , \t");
+    ch_ptr=strtok (NULL, " , \t");
+
+    ch_ptr=strtok (NULL, " , \t");
+    vec_sa2.push_back(atof(ch_ptr));
+
+    ch_ptr=strtok (NULL, " , \t");
+    vec_sb2.push_back(atof(ch_ptr));
+
+    ch_ptr=strtok (NULL, " , \t");
+    vec_wab.push_back(atof(ch_ptr));
+  }
+
+  infile.close();
+  infile.clear();
+
+  return;
+}
+
+
+//read bf file
+void ReadFile_bf (const string &file_bf, vector<string> &vec_rs, vector<vector<vector<double> > > &BF)
+{
+  BF.clear(); vec_rs.clear();
+
+  igzstream infile (file_bf.c_str(), igzstream::in);
+  if (!infile) {cout<<"error! fail to open bf file: "<<file_bf<<endl; return;}
+
+  string line, rs, block;
+  vector<double> vec_bf;
+  vector<vector<double> > mat_bf;
+  char *ch_ptr;
+
+  size_t bf_size, flag_block;
+
+  getline(infile, line);
+
+  size_t t=0;
+  while (!safeGetline(infile, line).eof()) {
+    flag_block=0;
+
+    ch_ptr=strtok ((char *)line.c_str(), " , \t");
+    rs=ch_ptr;
+    vec_rs.push_back(rs);
+
+    ch_ptr=strtok (NULL, " , \t");
+    if (t==0) {
+      block=ch_ptr;
+    } else {
+      if (strcmp(ch_ptr, block.c_str() )!=0) {
+	flag_block=1;
+	block=ch_ptr;
+      }
+    }
+
+    ch_ptr=strtok (NULL, " , \t");
+    while (ch_ptr!=NULL) {
+      vec_bf.push_back(atof(ch_ptr));
+      ch_ptr=strtok (NULL, " , \t");
+    }
+
+    if (t==0) {
+      bf_size=vec_bf.size();
+    } else {
+      if (bf_size!=vec_bf.size()) {cout<<"error! unequal row size in bf file."<<endl;}
+    }
+
+    if (flag_block==0) {
+      mat_bf.push_back(vec_bf);
+    } else {
+      BF.push_back(mat_bf);
+      mat_bf.clear();
+    }
+    vec_bf.clear();
+
+    t++;
+  }
+
+  infile.close();
+  infile.clear();
+
+  return;
+}
+
+
+//read category files
+//read both continuous and discrete category file, record mapRS2catc
+void ReadFile_cat (const string &file_cat, const vector<string> &vec_rs, gsl_matrix *Ac, gsl_matrix_int *Ad, gsl_vector_int *dlevel, size_t &kc, size_t &kd)
+{
+  igzstream infile (file_cat.c_str(), igzstream::in);
+  if (!infile) {cout<<"error! fail to open category file: "<<file_cat<<endl; return;}
+
+  string line;
+  char *ch_ptr;
+
+  string rs, chr, a1, a0, pos, cm;
+
+  //read header
+  HEADER header;
+  !safeGetline(infile, line).eof();
+  ReadHeader (line, header);
+
+  //use the header to determine the number of categories
+  kc=header.catc_col.size(); kd=header.catd_col.size();
+
+  //set up storage and mapper
+  map<string, vector<double> > mapRS2catc;
+  map<string, vector<int> > mapRS2catd;
+  vector<double> catc;
+  vector<int> catd;
+
+  //read the following lines to record mapRS2cat
+  while (!safeGetline(infile, line).eof()) {
+    ch_ptr=strtok ((char *)line.c_str(), " , \t");
+
+    if (header.rs_col==0) {
+      rs=chr+":"+pos;
+    }
+
+    catc.clear(); catd.clear();
+
+    for (size_t i=0; i<header.coln; i++) {
+      if (header.rs_col!=0 && header.rs_col==i+1) {
+	rs=ch_ptr;
+      } else if (header.chr_col!=0 && header.chr_col==i+1) {
+	chr=ch_ptr;
+      } else if (header.pos_col!=0 && header.pos_col==i+1) {
+	pos=ch_ptr;
+      } else if (header.cm_col!=0 && header.cm_col==i+1) {
+	cm=ch_ptr;
+      } else if (header.a1_col!=0 && header.a1_col==i+1) {
+	a1=ch_ptr;
+      } else if (header.a0_col!=0 && header.a0_col==i+1) {
+	a0=ch_ptr;
+      } else if (header.catc_col.size()!=0 && header.catc_col.count(i+1)!=0 ) {
+	catc.push_back(atof(ch_ptr));
+      } else if (header.catd_col.size()!=0 && header.catd_col.count(i+1)!=0 ) {
+	catd.push_back(atoi(ch_ptr));
+      } else {}
+
+      ch_ptr=strtok (NULL, " , \t");
+    }
+
+    if (mapRS2catc.count(rs)==0 && kc>0) {mapRS2catc[rs]=catc;}
+    if (mapRS2catd.count(rs)==0 && kd>0) {mapRS2catd[rs]=catd;}
+  }
+
+  //load into Ad and Ac
+  if (kc>0) {
+    Ac=gsl_matrix_alloc(vec_rs.size(), kc);
+    for (size_t i=0; i<vec_rs.size(); i++) {
+      if (mapRS2catc.count(vec_rs[i])!=0) {
+	for (size_t j=0; j<kc; j++) {
+	  gsl_matrix_set(Ac, i, j, mapRS2catc[vec_rs[i]][j]);
+	}
+      } else {
+	for (size_t j=0; j<kc; j++) {
+	  gsl_matrix_set(Ac, i, j, 0);
+	}
+      }
+    }
+  }
+
+  if (kd>0) {
+    Ad=gsl_matrix_int_alloc(vec_rs.size(), kd);
+
+    for (size_t i=0; i<vec_rs.size(); i++) {
+      if (mapRS2catd.count(vec_rs[i])!=0) {
+	for (size_t j=0; j<kd; j++) {
+	  gsl_matrix_int_set(Ad, i, j, mapRS2catd[vec_rs[i]][j]);
+	}
+      } else {
+	for (size_t j=0; j<kd; j++) {
+	  gsl_matrix_int_set(Ad, i, j, 0);
+	}
+      }
+    }
+
+    dlevel=gsl_vector_int_alloc(kd);
+    map<int, int> rcd;
+    int val;
+    for (size_t j=0; j<kd; j++) {
+      rcd.clear();
+      for (size_t i=0; i<Ad->size1; i++) {
+	val = gsl_matrix_int_get(Ad, i, j);
+	rcd[val] = 1;
+      }
+      gsl_vector_int_set (dlevel, j, rcd.size());
+    }
+  }
+
+  infile.clear();
+  infile.close();
+
+  return;
+}
+
+
+
+
+
+
+
+
+void BSLMMDAP::WriteResult (const gsl_matrix *Hyper, const gsl_matrix *BF)
+{
+  string file_bf, file_hyp;
+	file_bf=path_out+"/"+file_out;
+	file_bf+=".bf.txt";
+	file_hyp=path_out+"/"+file_out;
+	file_hyp+=".hyp.txt";
+
+	ofstream outfile_bf, outfile_hyp;
+
+	outfile_bf.open (file_bf.c_str(), ofstream::out);
+	outfile_hyp.open (file_hyp.c_str(), ofstream::out);
+
+	if (!outfile_bf) {cout<<"error writing file: "<<file_bf<<endl; return;}
+	if (!outfile_hyp) {cout<<"error writing file: "<<file_hyp<<endl; return;}
+
+	outfile_hyp<<"h"<<"\t"<<"rho"<<"\t"<<"sa2"<<"\t"<<"sb2"<<"\t"<<"weight"<<endl;
+	outfile_hyp<<scientific;
+	for (size_t i=0; i<Hyper->size1; i++) {
+	  for (size_t j=0; j<Hyper->size2; j++) {
+	    outfile_hyp<<setprecision(6)<<gsl_matrix_get (Hyper, i, j)<<"\t";
+	  }
+	  outfile_hyp<<endl;
+	}
+
+	outfile_bf<<"chr"<<"\t"<<"rs"<<"\t"<<"ps"<<"\t"<<"n_miss";
+	for (size_t i=0; i<BF->size2; i++) {
+	  outfile_bf<<"\t"<<"BF"<<i+1;
+	}
+	outfile_bf<<endl;
+
+	size_t t=0;
+	for (size_t i=0; i<ns_total; ++i) {
+	  if (indicator_snp[i]==0) {continue;}
+
+	  outfile_bf<<snpInfo[i].chr<<"\t"<<snpInfo[i].rs_number<<"\t"
+		    <<snpInfo[i].base_position<<"\t"<<snpInfo[i].n_miss;
+
+	  outfile_bf<<scientific;
+	  for (size_t j=0; j<BF->size2; j++) {
+	    outfile_bf<<"\t"<<setprecision(6)<<gsl_matrix_get (BF, t, j);
+	  }
+	  outfile_bf<<endl;
+
+	  t++;
+	}
+
+	outfile_hyp.close();
+	outfile_hyp.clear();
+	outfile_bf.close();
+	outfile_bf.clear();
+	return;
+}
+
+
+
+void BSLMMDAP::WriteResult (const vector<string> &vec_rs, const gsl_matrix *Hyper, const gsl_vector *pip, const gsl_vector *coef)
+{
+  string file_gamma, file_hyp, file_coef;
+	file_gamma=path_out+"/"+file_out;
+	file_gamma+=".gamma.txt";
+	file_hyp=path_out+"/"+file_out;
+	file_hyp+=".hyp.txt";
+	file_coef=path_out+"/"+file_out;
+	file_coef+=".coef.txt";
+
+	ofstream outfile_gamma, outfile_hyp, outfile_coef;
+
+	outfile_gamma.open (file_gamma.c_str(), ofstream::out);
+	outfile_hyp.open (file_hyp.c_str(), ofstream::out);
+	outfile_coef.open (file_coef.c_str(), ofstream::out);
+
+	if (!outfile_gamma) {cout<<"error writing file: "<<file_gamma<<endl; return;}
+	if (!outfile_hyp) {cout<<"error writing file: "<<file_hyp<<endl; return;}
+	if (!outfile_coef) {cout<<"error writing file: "<<file_coef<<endl; return;}
+
+	outfile_hyp<<"h"<<"\t"<<"rho"<<"\t"<<"sa2"<<"\t"<<"sb2"<<"\t"<<"weight"<<endl;
+	outfile_hyp<<scientific;
+	for (size_t i=0; i<Hyper->size1; i++) {
+	  for (size_t j=0; j<Hyper->size2; j++) {
+	    outfile_hyp<<setprecision(6)<<gsl_matrix_get (Hyper, i, j)<<"\t";
+	  }
+	  outfile_hyp<<endl;
+	}
+
+
+	outfile_gamma<<"rs"<<"\t"<<"gamma"<<endl;
+	for (size_t i=0; i<vec_rs.size(); ++i) {
+	  outfile_gamma<<vec_rs[i]<<"\t"<<scientific<<setprecision(6)<<gsl_vector_get(pip, i)<<endl;
+	}
+
+	outfile_coef<<"coef"<<endl;
+	outfile_coef<<scientific;
+	for (size_t i=0; i<coef->size; i++) {
+	  outfile_coef<<setprecision(6)<<gsl_vector_get (coef, i)<<endl;
+	}
+
+	outfile_coef.close();
+	outfile_coef.clear();
+	outfile_hyp.close();
+	outfile_hyp.clear();
+	outfile_gamma.close();
+	outfile_gamma.clear();
+	return;
+}
+
+
+
+
+/*
+void BSLMMDAP::SetXgamma (gsl_matrix *Xgamma, const gsl_matrix *X, vector<size_t> &rank)
+{
+	size_t pos;
+	for (size_t i=0; i<rank.size(); ++i) {
+		pos=mapRank2pos[rank[i]];
+		gsl_vector_view Xgamma_col=gsl_matrix_column (Xgamma, i);
+		gsl_vector_const_view X_col=gsl_matrix_const_column (X, pos);
+		gsl_vector_memcpy (&Xgamma_col.vector, &X_col.vector);
+	}
+
+	return;
+}
+*/
+
+double BSLMMDAP::CalcMarginal (const gsl_vector *Uty, const gsl_vector *K_eval, const double sigma_b2, const double tau)
+{
+	gsl_vector *weight_Hi=gsl_vector_alloc (Uty->size);
+
+	double logm=0.0;
+	double d, uy, Hi_yy=0, logdet_H=0.0;
+	for (size_t i=0; i<ni_test; ++i) {
+		d=gsl_vector_get (K_eval, i)*sigma_b2;
+		d=1.0/(d+1.0);
+		gsl_vector_set (weight_Hi, i, d);
+
+		logdet_H-=log(d);
+		uy=gsl_vector_get (Uty, i);
+		Hi_yy+=d*uy*uy;
+	}
+
+	//calculate likelihood
+	logm=-0.5*logdet_H-0.5*tau*Hi_yy+0.5*log(tau)*(double)ni_test;
+
+	gsl_vector_free (weight_Hi);
+
+	return logm;
+}
+
+
+double BSLMMDAP::CalcMarginal (const gsl_matrix *UtXgamma, const gsl_vector *Uty, const gsl_vector *K_eval, const double sigma_a2, const double sigma_b2, const double tau)
+{
+  clock_t  time_start;
+	double logm=0.0;
+	double d, uy, P_yy=0, logdet_O=0.0, logdet_H=0.0;
+
+	gsl_matrix *UtXgamma_eval=gsl_matrix_alloc (UtXgamma->size1, UtXgamma->size2);
+	gsl_matrix *Omega=gsl_matrix_alloc (UtXgamma->size2, UtXgamma->size2);
+	gsl_vector *XtHiy=gsl_vector_alloc (UtXgamma->size2);
+	gsl_vector *beta_hat=gsl_vector_alloc (UtXgamma->size2);
+	gsl_vector *weight_Hi=gsl_vector_alloc (UtXgamma->size1);
+
+	gsl_matrix_memcpy (UtXgamma_eval, UtXgamma);
+
+	logdet_H=0.0; P_yy=0.0;
+	for (size_t i=0; i<ni_test; ++i) {
+		gsl_vector_view UtXgamma_row=gsl_matrix_row (UtXgamma_eval, i);
+		d=gsl_vector_get (K_eval, i)*sigma_b2;
+		d=1.0/(d+1.0);
+		gsl_vector_set (weight_Hi, i, d);
+
+		logdet_H-=log(d);
+		uy=gsl_vector_get (Uty, i);
+		P_yy+=d*uy*uy;
+		gsl_vector_scale (&UtXgamma_row.vector, d);
+	}
+
+	//calculate Omega
+	gsl_matrix_set_identity (Omega);
+
+	time_start=clock();
+#ifdef WITH_LAPACK
+	lapack_dgemm ((char *)"T", (char *)"N", sigma_a2, UtXgamma_eval, UtXgamma, 1.0, Omega);
+#else
+	gsl_blas_dgemm (CblasTrans, CblasNoTrans, sigma_a2, UtXgamma_eval, UtXgamma, 1.0, Omega);
+#endif
+	time_Omega+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
+
+	//calculate beta_hat
+	gsl_blas_dgemv (CblasTrans, 1.0, UtXgamma_eval, Uty, 0.0, XtHiy);
+
+	logdet_O=CholeskySolve(Omega, XtHiy, beta_hat);
+
+	gsl_vector_scale (beta_hat, sigma_a2);
+
+	gsl_blas_ddot (XtHiy, beta_hat, &d);
+	P_yy-=d;
+
+	gsl_matrix_free (UtXgamma_eval);
+	gsl_matrix_free (Omega);
+	gsl_vector_free (XtHiy);
+	gsl_vector_free (beta_hat);
+	gsl_vector_free (weight_Hi);
+
+	logm=-0.5*logdet_H-0.5*logdet_O-0.5*tau*P_yy+0.5*log(tau)*(double)ni_test;
+
+	return logm;
+}
+
+
+double BSLMMDAP::CalcPrior (class HYPBSLMM &cHyp) {
+  double logprior=0;
+  logprior=((double)cHyp.n_gamma-1.0)*cHyp.logp+((double)ns_test-(double)cHyp.n_gamma)*log(1.0-exp(cHyp.logp));
+  return logprior;
+}
+
+
+//where A is the ni_test by n_cat matrix of annotations
+void BSLMMDAP::DAP_CalcBF (const gsl_matrix *U, const gsl_matrix *UtX, const gsl_vector *Uty, const gsl_vector *K_eval, const gsl_vector *y) {
+	clock_t time_start;
+
+	//set up BF
+	double tau, h, rho, sigma_a2, sigma_b2, d;
+	size_t ns_causal=10;
+	size_t n_grid=h_ngrid*rho_ngrid;
+	vector<double> vec_sa2, vec_sb2, logm_null;
+
+	gsl_matrix *BF=gsl_matrix_alloc(ns_test, n_grid);
+	gsl_matrix *Xgamma=gsl_matrix_alloc(ni_test, 1);
+	gsl_matrix *Hyper=gsl_matrix_alloc(n_grid, 5);
+
+	//compute tau by using yty
+	gsl_blas_ddot (Uty, Uty, &tau);
+	tau=(double)ni_test/tau;
+
+	//set up grid values for sigma_a2 and sigma_b2 based on an approximately even grid for h and rho, and a fixed number of causals
+	size_t ij=0;
+	for (size_t i=0; i<h_ngrid; i++) {
+	  h=h_min+(h_max-h_min)*(double)i/((double)h_ngrid-1);
+	  for (size_t j=0; j<rho_ngrid; j++) {
+	    rho=rho_min+(rho_max-rho_min)*(double)j/((double)rho_ngrid-1);
+
+	    sigma_a2=h*rho/((1-h)*(double)ns_causal);
+	    sigma_b2=h*(1.0-rho)/(trace_G*(1-h));
+
+	    vec_sa2.push_back(sigma_a2);
+	    vec_sb2.push_back(sigma_b2);
+	    logm_null.push_back(CalcMarginal (Uty, K_eval, 0.0, tau));
+
+	    gsl_matrix_set (Hyper, ij, 0, h);
+	    gsl_matrix_set (Hyper, ij, 1, rho);
+	    gsl_matrix_set (Hyper, ij, 2, sigma_a2);
+	    gsl_matrix_set (Hyper, ij, 3, sigma_b2);
+	    gsl_matrix_set (Hyper, ij, 4, 1/(double)n_grid);
+	    ij++;
+	  }
+	}
+
+	//compute BF factors
+	time_start=clock();
+	cout<<"Calculating BF..."<<endl;
+	for (size_t t=0; t<ns_test; t++) {
+	  gsl_vector_view Xgamma_col=gsl_matrix_column (Xgamma, 0);
+	  gsl_vector_const_view X_col=gsl_matrix_const_column (UtX, t);
+	  gsl_vector_memcpy (&Xgamma_col.vector, &X_col.vector);
+
+	  for (size_t ij=0; ij<n_grid; ij++) {
+	    sigma_a2=vec_sa2[ij];
+	    sigma_b2=vec_sb2[ij];
+
+	    d=CalcMarginal (Xgamma, Uty, K_eval, sigma_a2, sigma_b2, tau);
+	    d-=logm_null[ij];
+	    d=exp(d);
+
+	    gsl_matrix_set(BF, t, ij, d);
+	  }
+	}
+	time_Proposal=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
+
+	//save results
+	WriteResult (Hyper, BF);
+
+	//free matrices and vectors
+	gsl_matrix_free(BF);
+	gsl_matrix_free(Xgamma);
+	gsl_matrix_free(Hyper);
+	return;
+}
+
+
+
+
+
+void single_ct_regression(const gsl_matrix_int *Xd, const gsl_vector_int *dlevel, const gsl_vector *pip_vec, gsl_vector *coef, gsl_vector *prior_vec) {
+
+  map<int,double> sum_pip;
+  map<int,double> sum;
+
+  int levels = gsl_vector_int_get(dlevel,0);
+
+  for(int i=0;i<levels;i++){
+    sum_pip[i] = sum[i] = 0;
+  }
+
+  for(int i=0;i<Xd->size1;i++){
+    int cat = gsl_matrix_int_get(Xd,i,0);
+    sum_pip[cat] += gsl_vector_get(pip_vec,i);
+    sum[cat] += 1;
+  }
+
+  for(int i=0;i<Xd->size1;i++){
+    int cat = gsl_matrix_int_get(Xd,i,0);
+    gsl_vector_set(prior_vec,i,sum_pip[cat]/sum[cat]);
+  }
+
+  //double baseline=0;
+  for(int i=0;i<levels;i++){
+    double new_prior = sum_pip[i]/sum[i];
+    //gsl_vector_set(coef, i, log(new_prior/(1-new_prior))-baseline);
+    //if(i==0){
+    //baseline = log(new_prior/(1-new_prior));
+    //}
+    gsl_vector_set(coef, i, log(new_prior/(1-new_prior)) );
+  }
+
+  return;
+}
+
+
+
+
+//where A is the ni_test by n_cat matrix of annotations
+void BSLMMDAP::DAP_EstimateHyper (const size_t kc, const size_t kd, const vector<string> &vec_rs, const vector<double> &vec_sa2, const vector<double> &vec_sb2, const vector<double> &wab, const vector<vector<vector<double> > > &BF, gsl_matrix *Ac, gsl_matrix_int *Ad, gsl_vector_int *dlevel) {
+	clock_t time_start;
+
+	//set up BF
+	double h, rho, sigma_a2, sigma_b2, d, s, logm, logm_save;
+	size_t t1, t2;
+	size_t n_grid=wab.size(), ns_test=vec_rs.size();
+
+	gsl_vector *prior_vec=gsl_vector_alloc(ns_test);
+	gsl_matrix *Hyper=gsl_matrix_alloc(n_grid, 5);
+	gsl_vector *pip=gsl_vector_alloc(ns_test);
+	gsl_vector *coef=gsl_vector_alloc(kc+kd+1);
+
+	//perform the EM algorithm
+	vector<double> vec_wab, vec_wab_new;
+
+	//initial values
+	for (size_t t=0; t<ns_test; t++) {
+	  gsl_vector_set (prior_vec, t, (double)BF.size()/(double)ns_test);
+	}
+	for (size_t ij=0; ij<n_grid; ij++) {
+	  vec_wab.push_back(wab[ij]);
+	  vec_wab_new.push_back(wab[ij]);
+	}
+
+	//EM iteration
+	size_t it=0;
+	double dif=1;
+	while (it<100 && dif>1e-3) {
+	  //update E_gamma
+	  t1=0, t2=0;
+	  for (size_t b=0; b<BF.size(); b++) {
+	    s=1;
+	    for (size_t m=0; m<BF[b].size(); m++) {
+	      d=0;
+	      for (size_t ij=0; ij<n_grid; ij++) {
+		d+=vec_wab_new[ij]*BF[b][m][ij];
+	      }
+	      d*=gsl_vector_get(prior_vec, t1)/(1-gsl_vector_get(prior_vec, t1));
+
+	      gsl_vector_set(pip, t1, d);
+	      s+=d;
+	      t1++;
+	    }
+
+	    for (size_t m=0; m<BF[b].size(); m++) {
+	      d=gsl_vector_get(pip, t2)/s;
+	      gsl_vector_set(pip, t2, d);
+	      t2++;
+	    }
+	  }
+
+	  //update E_wab
+	  s=0;
+	  for (size_t ij=0; ij<n_grid; ij++) {
+	    vec_wab_new[ij]=0;
+
+	    t1=0;
+	    for (size_t b=0; b<BF.size(); b++) {
+	      d=1;
+	      for (size_t m=0; m<BF[b].size(); m++) {
+		d+=gsl_vector_get(prior_vec, t1)/(1-gsl_vector_get(prior_vec, t1))*vec_wab[ij]*BF[b][m][ij];
+		t1++;
+	      }
+	      vec_wab_new[ij]+=log(d);
+	    }
+
+	    s=max(s, vec_wab_new[ij]);
+	  }
+
+	  d=0;
+	  for (size_t ij=0; ij<n_grid; ij++) {
+	    vec_wab_new[ij]=exp(vec_wab_new[ij]-s);
+	    d+=vec_wab_new[ij];
+	  }
+
+	  for (size_t ij=0; ij<n_grid; ij++) {
+	    vec_wab_new[ij]/=d;
+	    //	    vec_wab[ij]=vec_wab_new[ij];
+	  }
+
+	  //update coef, and pi
+	  if(kc==0 && kd==0){//no annotation
+	    s=0;
+	    for (size_t t=0; t<pip->size; t++) {
+	      s+=gsl_vector_get(pip, t);
+	    }
+	    s=s/(double)pip->size;
+	    for (size_t t=0; t<pip->size; t++) {
+	      gsl_vector_set(prior_vec, t, s);
+	    }
+
+	    gsl_vector_set (coef, 0, log(s/(1-s)));
+	  } else if(kc==0 && kd!=0){//only discrete annotations
+	    if(kd == 1){
+	      single_ct_regression(Ad, dlevel, pip, coef, prior_vec);
+	    }else{
+	      logistic_cat_fit(coef, Ad, dlevel, pip, 0, 0);
+	      logistic_cat_pred(coef, Ad, dlevel, prior_vec);
+	    }
+	  } else if (kc!=0 && kd==0) {//only continuous annotations
+	    logistic_cont_fit(coef, Ac, pip, 0, 0);
+	    logistic_cont_pred(coef, Ac, prior_vec);
+	  } else if (kc!=0 && kd!=0) {//both continuous and categorical annotations
+	    logistic_mixed_fit(coef, Ad, dlevel, Ac, pip, 0, 0);
+	    logistic_mixed_pred(coef, Ad, dlevel, Ac, prior_vec);
+	  }
+
+	  //compute marginal likelihood
+	  logm=0;
+
+	  t1=0;
+	  for (size_t b=0; b<BF.size(); b++) {
+	    d=1; s=0;
+	    for (size_t m=0; m<BF[b].size(); m++) {
+	      s+=log(1-gsl_vector_get(prior_vec, t1));
+	      for (size_t ij=0; ij<n_grid; ij++) {
+		d+=gsl_vector_get(prior_vec, t1)/(1-gsl_vector_get(prior_vec, t1))*vec_wab[ij]*BF[b][m][ij];
+	      }
+	    }
+	    logm+=log(d)+s;
+	    t1++;
+	  }
+
+	  if (it>0) {
+	    dif=logm-logm_save;
+	  }
+	  logm_save=logm;
+	  it++;
+
+	  cout<<"iteration = "<<it<<"; marginal likelihood = "<<logm<<endl;
+	}
+
+	//update h and rho that correspond to w_ab
+	for (size_t ij=0; ij<n_grid; ij++) {
+	  sigma_a2=vec_sa2[ij];
+	  sigma_b2=vec_sb2[ij];
+
+	  d=exp(gsl_vector_get(coef, coef->size-1))/(1+exp(gsl_vector_get(coef, coef->size-1)));
+	  h=(d*(double)ns_test*sigma_a2+1*sigma_b2)/(1+d*(double)ns_test*sigma_a2+1*sigma_b2);
+	  rho=d*(double)ns_test*sigma_a2/(d*(double)ns_test*sigma_a2+1*sigma_b2);
+
+	  gsl_matrix_set (Hyper, ij, 0, h);
+	  gsl_matrix_set (Hyper, ij, 1, rho);
+	  gsl_matrix_set (Hyper, ij, 2, sigma_a2);
+	  gsl_matrix_set (Hyper, ij, 3, sigma_b2);
+	  gsl_matrix_set (Hyper, ij, 4, vec_wab_new[ij]);
+	}
+
+	//obtain beta and alpha parameters
+
+
+	//save results
+	WriteResult (vec_rs, Hyper, pip, coef);
+
+	//free matrices and vectors
+	gsl_vector_free(prior_vec);
+	gsl_matrix_free(Hyper);
+	gsl_vector_free(pip);
+	gsl_vector_free(coef);
+	return;
+}
+
+/*
+//readin the estimated hyper-parameters and perform fine mapping for each region
+void BSLMM::DAP_FineMapping (const gsl_matrix *U, const gsl_matrix *UtX, const gsl_matrix *A, const gsl_vector *Uty, const gsl_vector *K_eval, const gsl_vector *y, gsl_matrix *Hyper, gsl_vector *alpha, gsl_vector *pip) {
+	clock_t time_start;
+
+	//two priority sets: S_1 contains all candidate causal SNPs; S_2 contains the prioritized combintion of them
+	//two marginal probability sets: P_1 contains marginals for S_1; P_2 contains marginals for S_2;
+	set<size_t> S1set, S2set;
+	vector<size_t> S1vec;
+	vector<set<size_t> > S2vec;
+	vector<double> P1, P2;
+
+	//calculate P0 (null) and P1 (for every SNP)
+
+
+
+	//loop through the number of combinations
+	for (size_t s=0; s<p; s++) {
+	  //if (s==0), set up S_1: compute marginal of the null model, then compute P_1, then compute BF_1 and use them to select S_1; compute C_1
+
+
+
+	  //if (s==1), set up S_2: compute pair-wise P_2 and use them to select S_2; compute C_2
+
+	  //otherwise, match each combination of S_2 with each SNP from S_1, select into S_3; and replace S_2 with S_3; compute C_s
+
+
+	  //stop when the stopping critieria are reached (if S_2 is empty; if t; if kappa); add the residual component R
+
+	for (size_t t=0; t<total_step; ++t) {
+		if (t%d_pace==0 || t==total_step-1) {ProgressBar ("Running MCMC ", t, total_step-1, (double)n_accept/(double)(t*n_mh+1));}
+//		if (t>10) {break;}
+
+		if (a_mode==13) {
+			SampleZ (y, z_hat, z);
+			mean_z=CenterVector (z);
+
+			time_start=clock();
+			gsl_blas_dgemv (CblasTrans, 1.0, U, z, 0.0, Utz);
+			time_UtZ+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
+
+			//First proposal
+			if (cHyp_old.n_gamma==0 || cHyp_old.rho==0) {
+				logPost_old=CalcPosterior(Utz, K_eval, Utu_old, alpha_old, cHyp_old);
+				beta_old.clear();
+				for (size_t i=0; i<cHyp_old.n_gamma; ++i) {
+				  beta_old.push_back(0);
+				}
+			}
+			else {
+				gsl_matrix *UtXgamma=gsl_matrix_alloc (ni_test, cHyp_old.n_gamma);
+				gsl_vector *beta=gsl_vector_alloc (cHyp_old.n_gamma);
+				SetXgamma (UtXgamma, UtX, rank_old);
+				logPost_old=CalcPosterior(UtXgamma, Utz, K_eval, UtXb_old, Utu_old, alpha_old, beta, cHyp_old);
+
+				beta_old.clear();
+				for (size_t i=0; i<beta->size; ++i) {
+					beta_old.push_back(gsl_vector_get(beta, i));
+				}
+				gsl_matrix_free (UtXgamma);
+				gsl_vector_free (beta);
+			}
+		}
+
+
+	delete [] p_gamma;
+	beta_g.clear();
+
+	return;
+}
+
+*/
+
+
+
+
+
+
+/*
+//below fits MCMC for rho=1
+void BSLMM::CalcXtX (const gsl_matrix *X, const gsl_vector *y, const size_t s_size, gsl_matrix *XtX, gsl_vector *Xty)
+{
+  time_t time_start=clock();
+  gsl_matrix_const_view X_sub=gsl_matrix_const_submatrix(X, 0, 0, X->size1, s_size);
+  gsl_matrix_view XtX_sub=gsl_matrix_submatrix(XtX, 0, 0, s_size, s_size);
+  gsl_vector_view Xty_sub=gsl_vector_subvector(Xty, 0, s_size);
+
+#ifdef WITH_LAPACK
+  lapack_dgemm ((char *)"T", (char *)"N", 1.0, &X_sub.matrix, &X_sub.matrix, 0.0, &XtX_sub.matrix);
+#else
+  gsl_blas_dgemm (CblasTrans, CblasNoTrans, 1.0, &X_sub.matrix, &X_sub.matrix, 0.0, &XtX_sub.matrix);
+#endif
+  gsl_blas_dgemv(CblasTrans, 1.0, &X_sub.matrix, y, 0.0, &Xty_sub.vector);
+
+  time_Omega+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
+
+  return;
+}
+
+
+
+double BSLMM::CalcPosterior (const double yty, class HYPBSLMM &cHyp)
+{
+	double logpost=0.0;
+
+	//for quantitative traits, calculate pve and pge
+	//pve and pge for case/control data are calculted in CalcCC_PVEnZ
+	if (a_mode==11) {
+		cHyp.pve=0.0;
+		cHyp.pge=1.0;
+	}
+
+	//calculate likelihood
+	if (a_mode==11) {logpost-=0.5*(double)ni_test*log(yty);}
+	else {logpost-=0.5*yty;}
+
+	logpost+=((double)cHyp.n_gamma-1.0)*cHyp.logp+((double)ns_test-(double)cHyp.n_gamma)*log(1-exp(cHyp.logp));
+
+	return logpost;
+}
+
+
+double BSLMM::CalcPosterior (const gsl_matrix *Xgamma, const gsl_matrix *XtX, const gsl_vector *Xty, const double yty, const size_t s_size, gsl_vector *Xb, gsl_vector *beta, class HYPBSLMM &cHyp)
+{
+	double sigma_a2=cHyp.h/( (1-cHyp.h)*exp(cHyp.logp)*(double)ns_test);
+	double logpost=0.0;
+	double d, P_yy=yty, logdet_O=0.0;
+
+	gsl_matrix_const_view Xgamma_sub=gsl_matrix_const_submatrix (Xgamma, 0, 0, Xgamma->size1, s_size);
+	gsl_matrix_const_view XtX_sub=gsl_matrix_const_submatrix (XtX, 0, 0, s_size, s_size);
+	gsl_vector_const_view Xty_sub=gsl_vector_const_subvector (Xty, 0, s_size);
+
+	gsl_matrix *Omega=gsl_matrix_alloc (s_size, s_size);
+	gsl_matrix *M_temp=gsl_matrix_alloc (s_size, s_size);
+	gsl_vector *beta_hat=gsl_vector_alloc (s_size);
+	gsl_vector *Xty_temp=gsl_vector_alloc (s_size);
+
+	gsl_vector_memcpy (Xty_temp, &Xty_sub.vector);
+
+	//calculate Omega
+	gsl_matrix_memcpy (Omega, &XtX_sub.matrix);
+	gsl_matrix_scale (Omega, sigma_a2);
+	gsl_matrix_set_identity (M_temp);
+	gsl_matrix_add (Omega, M_temp);
+
+	//calculate beta_hat
+	logdet_O=CholeskySolve(Omega, Xty_temp, beta_hat);
+	gsl_vector_scale (beta_hat, sigma_a2);
+
+	gsl_blas_ddot (Xty_temp, beta_hat, &d);
+	P_yy-=d;
+
+	//sample tau
+	double tau=1.0;
+	if (a_mode==11) {tau =gsl_ran_gamma (gsl_r, (double)ni_test/2.0,  2.0/P_yy); }
+
+	//sample beta
+	for (size_t i=0; i<s_size; i++)
+	{
+		d=gsl_ran_gaussian(gsl_r, 1);
+		gsl_vector_set(beta, i, d);
+	}
+	gsl_vector_view beta_sub=gsl_vector_subvector(beta, 0, s_size);
+	gsl_blas_dtrsv(CblasUpper, CblasNoTrans, CblasNonUnit, Omega, &beta_sub.vector);
+
+	//it compuates inv(L^T(Omega)) %*% beta;
+	gsl_vector_scale(&beta_sub.vector, sqrt(sigma_a2/tau));
+	gsl_vector_add(&beta_sub.vector, beta_hat);
+	gsl_blas_dgemv (CblasNoTrans, 1.0, &Xgamma_sub.matrix, &beta_sub.vector, 0.0, Xb);
+
+	//for quantitative traits, calculate pve and pge
+	if (a_mode==11) {
+		gsl_blas_ddot (Xb, Xb, &d);
+		cHyp.pve=d/(double)ni_test;
+		cHyp.pve/=cHyp.pve+1.0/tau;
+		cHyp.pge=1.0;
+	}
+
+	logpost=-0.5*logdet_O;
+	if (a_mode==11) {logpost-=0.5*(double)ni_test*log(P_yy);}
+	else {logpost-=0.5*P_yy;}
+
+	logpost+=((double)cHyp.n_gamma-1.0)*cHyp.logp+((double)ns_test-(double)cHyp.n_gamma)*log(1.0-exp(cHyp.logp));
+
+	gsl_matrix_free (Omega);
+	gsl_matrix_free (M_temp);
+	gsl_vector_free (beta_hat);
+	gsl_vector_free (Xty_temp);
+
+	return logpost;
+}
+*/
+
+