void PowerPosteriorAnalysis::constructInternalObject( void )
{
// we free the memory first
delete value;
// now allocate a new sliding move
const RevBayesCore::Model& mdl = static_cast<const Model &>( model->getRevObject() ).getValue();
const WorkspaceVector<Move>& rlmvs = static_cast<const WorkspaceVector<Move> &>( moves->getRevObject() );
const WorkspaceVector<Monitor>& rlmntr = static_cast<const WorkspaceVector<Monitor> &>( monitors->getRevObject() );
RevBayesCore::RbVector<RevBayesCore::Monitor> mntr;
for ( size_t i = 0; i < rlmntr.size(); ++i )
{
mntr.push_back( rlmntr[i].getValue() );
}
RevBayesCore::RbVector<RevBayesCore::Move> mvs;
for ( size_t i = 0; i < rlmvs.size(); ++i )
{
mvs.push_back( rlmvs[i].getValue() );
}
const std::string& fn = static_cast<const RlString &>( filename->getRevObject() ).getValue();
const double alpha = static_cast<const RealPos &>( alphaVal->getRevObject() ).getValue();
const int sf = static_cast<const Natural &>( sampFreq->getRevObject() ).getValue();
const int k = static_cast<const Natural &>( proc_per_lik->getRevObject() ).getValue();
RevBayesCore::Mcmc *m = new RevBayesCore::Mcmc(mdl, mvs, mntr);
m->setScheduleType( "random" );
value = new RevBayesCore::PowerPosteriorAnalysis( m, fn, size_t(k) );
std::vector<double> beta;
if ( powers->getRevObject() != RevNullObject::getInstance() )
{
beta = static_cast<const ModelVector<RealPos> &>( powers->getRevObject() ).getValue();
}
else
{
int k = static_cast<const Natural &>( cats->getRevObject() ).getValue();
for (int i = k; i >= 0; --i)
{
double b = RevBayesCore::RbStatistics::Beta::quantile(alpha,1.0,i / double(k));
beta.push_back( b );
}
}
value->setPowers( beta );
value->setSampleFreq( sf );
}
RevPtr<RevVariable> Func_Mcmc::execute( void )
{
const RevBayesCore::Model& mdl = static_cast<const Model &>( args[0].getVariable()->getRevObject() ).getValue();
const WorkspaceVector<Monitor> & ws_vec_mntr = static_cast<const WorkspaceVector<Monitor> &>( args[1].getVariable()->getRevObject() );
RevBayesCore::RbVector<RevBayesCore::Monitor> mntr;
for ( size_t i = 0; i < ws_vec_mntr.size(); ++i )
{
mntr.push_back( ws_vec_mntr[i].getValue() );
}
const WorkspaceVector<Move>& ws_vec_mvs = static_cast<const WorkspaceVector<Move> &>( args[2].getVariable()->getRevObject() );
RevBayesCore::RbVector<RevBayesCore::Move> mvs;
for ( size_t i = 0; i < ws_vec_mvs.size(); ++i )
{
mvs.push_back( ws_vec_mvs[i].getValue() );
}
const std::string & sched = static_cast<const RlString &>( args[3].getVariable()->getRevObject() ).getValue();
int nreps = static_cast<const Natural &>( args[4].getVariable()->getRevObject() ).getValue();
RevBayesCore::Mcmc *m = new RevBayesCore::Mcmc(mdl, mvs, mntr);
m->setScheduleType( sched );
RevBayesCore::MonteCarloAnalysis *a = new RevBayesCore::MonteCarloAnalysis(m,nreps);
return new RevVariable( new MonteCarloAnalysis( a ) );
}