//----------------------------------------------------------------------
void BLCSSS::rwm_draw_chunk(int chunk){
clock_t start = clock();
const Selector &inc(m_->coef().inc());
int nvars = inc.nvars();
Vec full_nonzero_beta = m_->beta(); // only nonzero components
// Compute information matrix for proposal distribution. For
// efficiency, also compute the log-posterior of the current beta.
Vec mu(inc.select(pri_->mu()));
Spd siginv(inc.select(pri_->siginv()));
double original_logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true);
const std::vector<Ptr<BinomialRegressionData> > &data(m_->dat());
int nobs = data.size();
int full_chunk_size = compute_chunk_size();
int chunk_start = chunk * full_chunk_size;
int elements_remaining = nvars - chunk_start;
int this_chunk_size = std::min(elements_remaining, full_chunk_size);
Selector chunk_selector(nvars, false);
for(int i = chunk_start; i< chunk_start + this_chunk_size; ++i) {
chunk_selector.add(i);
}
Spd proposal_ivar = chunk_selector.select(siginv);
for(int i = 0; i < nobs; ++i){
Vec x = inc.select(data[i]->x());
double eta = x.dot(full_nonzero_beta);
double prob = plogis(eta);
double weight = prob * (1-prob);
VectorView x_chunk(x, chunk_start, this_chunk_size);
// Only upper triangle is accessed. Need to reflect at end of loop.
proposal_ivar.add_outer(x_chunk, weight, false);
int yi = data[i]->y();
int ni = data[i]->n();
original_logpost += dbinom(yi, ni, prob, true);
}
proposal_ivar.reflect();
VectorView beta_chunk(full_nonzero_beta, chunk_start, this_chunk_size);
if(tdf_ > 0){
beta_chunk = rmvt_ivar_mt(
rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_, tdf_);
}else{
beta_chunk = rmvn_ivar_mt(
rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_);
}
double logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true);
Vec full_beta(inc.expand(full_nonzero_beta));
logpost += m_->log_likelihood(full_beta, 0, 0, false);
double log_alpha = logpost - original_logpost;
double logu = log(runif_mt(rng()));
++rwm_chunk_attempts_;
if(logu < log_alpha){
m_->set_beta(full_nonzero_beta);
++rwm_chunk_successes_;
}
clock_t end = clock();
rwm_chunk_times_ += double(end - start) / CLOCKS_PER_SEC;
}
//----------------------------------------------------------------------
void BLCSSS::rwm_draw_chunk(int chunk){
const Selector &inc(m_->coef().inc());
int nvars = inc.nvars();
Vector full_nonzero_beta = m_->included_coefficients();
// Compute information matrix for proposal distribution. For
// efficiency, also compute the log-posterior of the current beta.
Vector mu(inc.select(pri_->mu()));
SpdMatrix siginv(inc.select(pri_->siginv()));
double original_logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true);
const std::vector<Ptr<BinomialRegressionData> > &data(m_->dat());
int nobs = data.size();
int full_chunk_size = compute_chunk_size(max_rwm_chunk_size_);
int chunk_start = chunk * full_chunk_size;
int elements_remaining = nvars - chunk_start;
int this_chunk_size = std::min(elements_remaining, full_chunk_size);
Selector chunk_selector(nvars, false);
for(int i = chunk_start; i< chunk_start + this_chunk_size; ++i) {
chunk_selector.add(i);
}
SpdMatrix proposal_ivar = chunk_selector.select(siginv);
for(int i = 0; i < nobs; ++i){
Vector x = inc.select(data[i]->x());
double eta = x.dot(full_nonzero_beta);
double prob = plogis(eta);
double weight = prob * (1-prob);
VectorView x_chunk(x, chunk_start, this_chunk_size);
// Only upper triangle is accessed. Need to reflect at end of loop.
proposal_ivar.add_outer(x_chunk, weight, false);
original_logpost += dbinom(data[i]->y(), data[i]->n(), prob, true);
}
proposal_ivar.reflect();
VectorView beta_chunk(full_nonzero_beta, chunk_start, this_chunk_size);
if(tdf_ > 0){
beta_chunk = rmvt_ivar_mt(
rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_, tdf_);
}else{
beta_chunk = rmvn_ivar_mt(
rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_);
}
double logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true);
Vector full_beta(inc.expand(full_nonzero_beta));
logpost += m_->log_likelihood(full_beta, 0, 0, false);
double log_alpha = logpost - original_logpost;
double logu = log(runif_mt(rng()));
if (logu < log_alpha) {
m_->set_included_coefficients(full_nonzero_beta);
move_accounting_.record_acceptance("rwm_chunk");
} else {
move_accounting_.record_rejection("rwm_chunk");
}
}
Vector rmvt_ivar(const Vector &mu, const SpdMatrix &ivar, double nu){
return rmvt_ivar_mt(GlobalRng::rng, mu, ivar, nu); }
Vec rmvt_ivar(const Vec &mu, const Spd &ivar, double nu){
return rmvt_ivar_mt(GlobalRng::rng, mu, ivar, nu); }