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RJupdates.cpp
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RJupdates.cpp
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// This program, BayeScan, aims at detecting genetics markers under selection,
// based on allele frequency differences between population.
// Copyright (C) 2010 Matthieu Foll
//
// 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 "global_defs.h"
#include <algorithm>
#include <math.h>
#include <omp.h>
//#define prop_alpha_mean 0
//#define prop_alpha_var 5
using namespace std;
void jump_model()
{
double A; // log of A in MH algorithm : accept move with probability min(1,A)
double r; // random value to accept/reject the move
double old_alpha;
double new_theta,old_theta; // new and old values of theta
#pragma omp parallel for SCHED_I /*reduction(+:nb_alpha_included)*/ private(r, A, old_alpha, new_theta,old_theta)
for (int i=0;i<I;i++) // cycle over loci
{
if (!discarded_loci[i])
{
old_alpha=alpha[i];
// propose new alpha value
if (!alpha_included[i])
alpha[i]=randgen_parallel[omp_get_thread_num()].randNorm(mean_alpha[i],sqrt(var_alpha[i]));
else
alpha[i]=0;
// change the state of alpha
alpha_included[i]=!alpha_included[i];
// calculate A
A=0;
for (int j=0;j<J;j++)
{
// calculate old and new value of theta
new_theta=exp(-(alpha[i]+beta[j]));
old_theta=exp(-(old_alpha+beta[j]));
A+= gammaln(new_theta)-gammaln(old_theta)
- gammaln(new_theta*freq_ancestral[i]) + gammaln(old_theta*freq_ancestral[i])
- gammaln(new_theta*(1-freq_ancestral[i])) + gammaln(old_theta*(1-freq_ancestral[i]))
+ freq_ancestral[i]*(new_theta-old_theta)*log(pop[j].locus[i].p)
+ (1-freq_ancestral[i])*(new_theta-old_theta)*log(1-pop[j].locus[i].p);
}
if (alpha_included[i]) //if we add parameter
A+= log_prior_alpha(alpha[i])//-0.5*log(2*M_PI*sd_prior_alpha*sd_prior_alpha)-(alpha[i]*alpha[i])/(2*sd_prior_alpha*sd_prior_alpha)
-(-0.5*log(2*M_PI*var_alpha[i])-((alpha[i]-mean_alpha[i])*(alpha[i]-mean_alpha[i]))/(2*var_alpha[i]))-log(prior_odds);
// inverse if we remove
else
A+= (-0.5*log(2*M_PI*var_alpha[i])-((alpha[i]-mean_alpha[i])*(alpha[i]-mean_alpha[i]))/(2*var_alpha[i]))
-log_prior_alpha(alpha[i])+log(prior_odds);//-(-0.5*log(2*M_PI*sd_prior_alpha*sd_prior_alpha)-(alpha[i]*alpha[i])/(2*sd_prior_alpha*sd_prior_alpha));
r=randgen_parallel[omp_get_thread_num()].randDblExc();
// reject proposed value
if (log(r)>A)
{
alpha[i]=old_alpha;
alpha_included[i]=!alpha_included[i];
}
/* else
{
if (alpha_included[i])
nb_alpha_included++;
else
nb_alpha_included--;
}*/
}
}
}
void jump_model_codominant()
{
double A; // log of A in MH algorithm : accept move with probability min(1,A)
double r; // random value to accept/reject the move
double old_alpha;
double new_theta,old_theta; // new and old values of theta
//double old_log_likelihood; // old loglikelihood
double diff_log_likelihood; // old loglikelihood
#pragma omp parallel for SCHED_I reduction(+:/*nb_alpha_included,*/log_likelihood) private(r, A, old_alpha, new_theta,old_theta,diff_log_likelihood)
for (int i=0;i<I;i++) // cycle over loci
{
if (!discarded_loci[i])
{
old_alpha=alpha[i];
// propose new alpha value
if (!alpha_included[i])
alpha[i]=randgen_parallel[omp_get_thread_num()].randNorm(mean_alpha[i],sqrt(var_alpha[i]));
else
alpha[i]=0;
// change the state of alpha
alpha_included[i]=!alpha_included[i];
// calculate A
A=0;
double old_l=0;
for (int j=0;j<J;j++)
{
// calculate old and new value of theta
old_theta=exp(-(old_alpha+beta[j]));
old_l+=gammaln(old_theta)-gammaln(pop[j].locus[i].alleleCount+old_theta);
for (int k=0;k<pop[j].locus[i].ar;k++)
old_l+=gammaln(pop[j].locus[i].data_allele_count[k]+old_theta*freq_locus[i].allele[k])
-gammaln(old_theta*freq_locus[i].allele[k]);
}
double new_l=0;
for (int j=0;j<J;j++)
{
// calculate old and new value of theta
new_theta=exp(-(alpha[i]+beta[j]));
new_l+=gammaln(new_theta)-gammaln(pop[j].locus[i].alleleCount+new_theta);
for (int k=0;k<pop[j].locus[i].ar;k++)
new_l+=gammaln(pop[j].locus[i].data_allele_count[k]+new_theta*freq_locus[i].allele[k])
-gammaln(new_theta*freq_locus[i].allele[k]);
}
// store the old loglikelihood and calculate the new loglikelihood
//old_log_likelihood=log_likelihood;
//log_likelihood=old_log_likelihood-old_l+new_l;
diff_log_likelihood=-old_l+new_l;
A=diff_log_likelihood;
if (alpha_included[i]) //if we add parameter
A+= log_prior_alpha(alpha[i])//-0.5*log(2*M_PI*sd_prior_alpha*sd_prior_alpha)-(alpha[i]*alpha[i])/(2*sd_prior_alpha*sd_prior_alpha)
-(-0.5*log(2*M_PI*var_alpha[i])-((alpha[i]-mean_alpha[i])*(alpha[i]-mean_alpha[i]))/(2*var_alpha[i]))-log(prior_odds);
// inverse if we remove
else
A+= (-0.5*log(2*M_PI*var_alpha[i])-((alpha[i]-mean_alpha[i])*(alpha[i]-mean_alpha[i]))/(2*var_alpha[i]))
-log_prior_alpha(alpha[i])+log(prior_odds);//-(-0.5*log(2*M_PI*sd_prior_alpha*sd_prior_alpha)-(alpha[i]*alpha[i])/(2*sd_prior_alpha*sd_prior_alpha));
r=randgen_parallel[omp_get_thread_num()].randDblExc();
// reject proposed value
if (log(r)>A)
{
alpha[i]=old_alpha;
alpha_included[i]=!alpha_included[i];
//log_likelihood=old_log_likelihood;
}
else
{
log_likelihood=log_likelihood+diff_log_likelihood;
/* if (alpha_included[i])
nb_alpha_included++;
else
nb_alpha_included--;*/
}
}
}
}