/**
* Perform the proposal.
*
* \return The hastings ratio.
*/
double EventBranchTimeBetaProposal::doProposal( void )
{
CharacterHistory &history = distribution->getCharacterHistory();
RandomNumberGenerator *rng = GLOBAL_RNG;
size_t num_events = history.getNumberEvents();
// we let the proposal fail if there is actually no event to slide
failed = (num_events == 0);
if ( failed == false )
{
// pick a random event
size_t branch_index = 0;
CharacterEvent *event = history.pickRandomEvent( branch_index );
// we need to remove and add the event so that the events are back in time order
history.removeEvent(event, branch_index);
double branch_length = distribution->getValue().getNode(branch_index).getBranchLength();
// store the event
stored_value = event;
// get the current index
stored_time = event->getTime();
// store the current branch
stored_branch_index = branch_index;
// draw new ages and compute the hastings ratio at the same time
double m = stored_time / branch_length;
double a = delta * m + offset;
double b = delta * (1.0-m) + offset;
double new_time = RbStatistics::Beta::rv(a, b, *rng);
// compute the Hastings ratio
double forward = RbStatistics::Beta::lnPdf(a, b, new_time);
double new_a = delta * new_time + offset;
double new_b = delta * (1.0-new_time) + offset;
double backward = RbStatistics::Beta::lnPdf(new_a, new_b, stored_time / branch_length);
// set the time
event->setTime( new_time * branch_length );
// we need to remove and add the event so that the events are back in time order
history.addEvent(event, branch_index);
return backward - forward;
}
else
{
// we need to decrement the failed counter because we did not actually reject the new proposal
move->decrementTriedCounter();
return RbConstants::Double::neginf;
}
return 0.0;
}
size_t HeterogeneousRateBirthDeath::computeStartIndex(size_t i) const
{
size_t node_index = i;
while ( value->getNode(node_index).isRoot() == false && branch_histories[node_index].getNumberEvents() == 0)
{
node_index = value->getNode(node_index).getParent().getIndex();
}
if ( value->getNode(node_index).isRoot() == false )
{
const BranchHistory &bh = branch_histories[ node_index ];
const std::multiset<CharacterEvent*, CharacterEventCompare> &h = bh.getHistory();
CharacterEvent *event = *(h.begin());
return event->getState();
}
else
{
return root_state->getValue()-1;
}
}
void HeterogeneousRateBirthDeath::executeMethod(const std::string &n, const std::vector<const DagNode *> &args, RbVector<double> &rv) const
{
if ( n == "averageSpeciationRate" )
{
size_t num_branches = branch_histories.getNumberBranches();
const RbVector<double> &lambda = speciation->getValue();
rv.clear();
rv.resize( num_branches );
for (size_t i = 0; i < num_branches; ++i)
{
const TopologyNode &node = this->value->getNode( i );
const BranchHistory& bh = branch_histories[ i ];
const std::multiset<CharacterEvent*,CharacterEventCompare>& hist = bh.getHistory();
size_t state_index_rootwards = computeStartIndex( node.getParent().getIndex() );
double rate = 0;
double begin_time = 0.0;
double branch_length = node.getBranchLength();
for (std::multiset<CharacterEvent*,CharacterEventCompare>::const_iterator it=hist.begin(); it!=hist.end(); ++it)
{
CharacterEvent* event = *it;
double end_time = event->getTime();
double time_interval = (end_time - begin_time) / branch_length;
// we need to set the current rate caterogy
size_t current_state = event->getState();
rate += time_interval * lambda[current_state];
begin_time = end_time;
}
rate += (branch_length-begin_time)/branch_length * lambda[state_index_rootwards];
rv[i] = rate;
}
}
else if ( n == "averageExtinctionRate" )
{
size_t num_branches = branch_histories.getNumberBranches();
const RbVector<double> &mu = extinction->getValue();
rv.clear();
rv.resize( num_branches );
for (size_t i = 0; i < num_branches; ++i)
{
const TopologyNode &node = this->value->getNode( i );
const BranchHistory& bh = branch_histories[ i ];
const std::multiset<CharacterEvent*,CharacterEventCompare>& hist = bh.getHistory();
size_t state_index_rootwards = computeStartIndex( node.getParent().getIndex() );
double rate = 0;
double begin_time = 0.0;
double branch_length = node.getBranchLength();
for (std::multiset<CharacterEvent*,CharacterEventCompare>::const_iterator it=hist.begin(); it!=hist.end(); ++it)
{
CharacterEvent* event = *it;
double end_time = event->getTime();
double time_interval = (end_time - begin_time) / branch_length;
// we need to set the current rate caterogy
size_t current_state = event->getState();
rate += time_interval * mu[current_state];
begin_time = end_time;
}
rate += (branch_length-begin_time)/branch_length * mu[state_index_rootwards];
rv[i] = rate;
}
}
else
{
throw RbException("The heterogeneous rate birth-death process does not have a member method called '" + n + "'.");
}
}
void HeterogeneousRateBirthDeath::computeNodeProbability(const RevBayesCore::TopologyNode &node, size_t node_index)
{
// check for recomputation
if ( dirty_nodes[node_index] || true )
{
// mark as computed
dirty_nodes[node_index] = false;
const BranchHistory& bh = branch_histories[ node_index ];
const std::multiset<CharacterEvent*,CharacterEventCompare>& hist = bh.getHistory();
// const std::vector<CharacterEvent*> child_states = bh.getChildCharacters();
// size_t start_index = child_states[0]->getState();
size_t state_index_tipwards = computeStartIndex( node_index );
size_t state_index_rootwards = computeStartIndex( node.getParent().getIndex() );
std::vector<double> initialState = std::vector<double>(1+num_rate_categories,0);
if ( node.isTip() )
{
// this is a tip node
double samplingProbability = rho->getValue();
for (size_t i=0; i<num_rate_categories; ++i)
{
initialState[i] = 1.0 - samplingProbability;
}
initialState[num_rate_categories] = samplingProbability;
}
else
{
// this is an internal node
const TopologyNode &left = node.getChild(0);
size_t left_index = left.getIndex();
computeNodeProbability( left, left_index );
const TopologyNode &right = node.getChild(1);
size_t right_index = right.getIndex();
computeNodeProbability( right, right_index );
// now compute the likelihoods of this internal node
const std::vector<double> &leftStates = nodeStates[left_index][activeLikelihood[left_index]];
const std::vector<double> &rightStates = nodeStates[right_index][activeLikelihood[right_index]];
const RbVector<double> &birthRate = speciation->getValue();
for (size_t i=0; i<num_rate_categories; ++i)
{
initialState[i] = leftStates[i];
}
initialState[num_rate_categories] = leftStates[num_rate_categories]*rightStates[num_rate_categories]*birthRate[ state_index_tipwards ];
}
const RbVector<double> &s = speciation->getValue();
const RbVector<double> &e = extinction->getValue();
double r = event_rate->getValue();
double beginAge = node.getAge();
// remember that we go back in time (rootwards)
double begin_time = 0.0;
double branch_length = node.getBranchLength();
// set the previous state to an impossible state
// we need this for checking if the states were different
size_t previous_state = num_rate_categories;
for (std::multiset<CharacterEvent*,CharacterEventCompare>::const_iterator it=hist.begin(); it!=hist.end(); ++it)
{
CharacterEvent* event = *it;
double end_time = event->getTime();
double time_interval = end_time - begin_time;
// we need to set the current rate category
size_t current_state = event->getState();
// check that we got a distinct new state
if ( previous_state == current_state )
{
shift_same_category = true;
}
updateBranchProbabilitiesNumerically(initialState, beginAge, beginAge+time_interval, s, e, r, current_state);
initialState[num_rate_categories] = initialState[num_rate_categories]*event_rate->getValue()* (1.0/num_rate_categories);
begin_time = end_time;
beginAge += time_interval;
previous_state = current_state;
}
// check that we got a distinct new state
if ( previous_state == state_index_rootwards )
{
shift_same_category = true;
}
double time_interval = branch_length - begin_time;
updateBranchProbabilitiesNumerically(initialState, beginAge, beginAge+time_interval, s, e, r, state_index_rootwards);
// rescale the states
double max = initialState[num_rate_categories];
initialState[num_rate_categories] = 1.0;
// totalScaling -= scalingFactors[node_index][activeLikelihood[node_index]];
// scalingFactors[node_index][activeLikelihood[node_index]] = log(max);
// totalScaling += scalingFactors[node_index][activeLikelihood[node_index]] - scalingFactors[node_index][activeLikelihood[node_index]^1];
// totalScaling += scalingFactors[node_index][activeLikelihood[node_index]];
totalScaling += log(max);
// store the states
nodeStates[node_index][activeLikelihood[node_index]] = initialState;
}
}