void DP_eta_theta(PARAM *param, PRIOR *prior, DATA *data, const gsl_rng *r, int pid, int *inuse) {
int i, j, id, accept;
float Delta, mhratio, newval, scale, tmp_lambda, tmp;
scale = prior->gamma_eta[pid] / (1.0 - prior->gamma_eta[pid]);
if(inuse[pid] == 0) {
newval = gsl_ran_exponential(r, prior->mean_eta) + 1.0;
Delta = newval - prior->theta_eta[pid];
prior->theta_eta[pid] = newval;
}
else {
/* metropolis-hastings */
mhratio = 0.0;
Delta = gsl_ran_gaussian(r, 1.0);
if(prior->theta_eta[pid] + Delta <= 1.0 || prior->theta_eta[pid] + Delta > 100.0) {
accept = 0;
}
else {
for(i=0;i<data->nprey;i++) {
if(prior->w_eta[i] == pid) {
for(j=0;j<data->preyNinter[i];j++) {
id = data->p2i[i][j];
if(param->Z[data->a2u[id]] && data->d[id] > 0.0) {
tmp_lambda = param->lambda_true[id];
// else tmp_lambda = param->lambda_false[id];
/* if(param->Z[data->a2u[id]]) */
tmp = data->d[id] < exp(param->lambda_false[id]) && param->lambda_false[id] < param->lambda_true[id] ? exp(param->lambda_false[id]) : data->d[id];
if(lowMode) {
mhratio += log_poisson_g_prop(GSL_MIN(_LM_,data->d[id]), exp(tmp_lambda), prior->theta_eta[pid]+Delta)
- log_poisson_g_prop(GSL_MIN(_LM_,data->d[id]), exp(tmp_lambda), prior->theta_eta[pid]);
}
else {
mhratio += log_poisson_g_prop(data->d[id], exp(tmp_lambda), prior->theta_eta[pid]+Delta)
- log_poisson_g_prop(data->d[id], exp(tmp_lambda), prior->theta_eta[pid]);
}
/* mhratio += log_poisson_g_prop(tmp, exp(tmp_lambda), prior->theta_eta[pid]+Delta)
- log_poisson_g_prop(tmp, exp(tmp_lambda), prior->theta_eta[pid]); */
}
}
}
}
mhratio += log(gsl_ran_exponential_pdf(prior->theta_eta[pid]+Delta-1.0, prior->mean_eta))
- log(gsl_ran_exponential_pdf(prior->theta_eta[pid]-1.0, prior->mean_eta));
// mhratio += -2.0 * (log(prior->theta_eta[pid]+ Delta) - log(prior->theta_eta[pid]));
accept = gsl_ran_flat(r, 0.0, 1.0) <= GSL_MIN(1.0, exp(mhratio)) ? 1 : 0 ;
}
/* if accepted, update param and lambda */
if(accept) {
prior->theta_eta[pid] += Delta;
for(i=0;i<data->nprey;i++) {
if(prior->w_eta[i] == pid) {
param->eta[i] += Delta;
}
}
}
}
}
/* if distributions are added here, the parameters they expected
must be listed in interface.R */
double logdist(double x, char* distribution, vec_double* params){
double out = 1.;
if (!strcmp(distribution, "flat")) {
out = gsl_ran_flat_pdf(x, params->values[0], params->values[1]);
} else if (!strcmp(distribution, "gaussian")) {
out = gsl_ran_gaussian_pdf(x - params->values[0], params->values[1]);
} else if (!strcmp(distribution, "lognormal")) {
out = gsl_ran_lognormal_pdf(x, params->values[0], params->values[1]);
} else if (!strcmp(distribution, "beta")) {
out = gsl_ran_beta_pdf(x, params->values[0], params->values[1]);
} else if (!strcmp(distribution, "exponential")) {
out = gsl_ran_exponential_pdf(x, params->values[0]);
}
out = log(out);
filter_logprob(&out);
return(out);
}
double pdf_Exponential(double x) { return gsl_ran_exponential_pdf(x,1); }
double
test_exponential_pdf (double x)
{
return gsl_ran_exponential_pdf (x, 2.0);
}
/* basic version returns NaN for mu=0; this one returns 0 */
double gsl_ran_exponential_pdf_fixed(double x, double mu){
if(mu <= NEARZERO) return 0.0;
return gsl_ran_exponential_pdf(x, mu);
}
RcppExport SEXP updatealphau_noPu_Exp(SEXP alphaut, SEXP n_s, SEXP n_u, SEXP I, SEXP K, SEXP lambda_u, SEXP var_p, SEXP ttt, SEXP gammat)
{
BEGIN_RCPP
Rcpp::IntegerMatrix xgammat(gammat);
Rcpp::NumericVector xalphaut(alphaut);
Rcpp::IntegerMatrix xn_s(n_s);
Rcpp::IntegerMatrix xn_u(n_u);
int xI = Rcpp::as<int>(I);
int xK = Rcpp::as<int>(K);
Rcpp::NumericVector sqrt_var(var_p);
int xtt = Rcpp::as<int>(ttt);
Rcpp::NumericVector xlambda_u(lambda_u);
Rcpp::IntegerVector xAalphau(xK);
Rcpp::RNGScope scope;
double delF = 0.0;
double log1 = 0.0;
double log2 = 0.0;
double sum_alphau = 0.0;
int flag1 = 0; int flag0 = 0; int flagkk = 0;
int lp0 = 0; int lp1 = 0;
double sum_nusalphau = 0.0;
double sum_nualphau = 0.0;
double sums = 0.;
for (int kk = 0; kk < xK; kk++) {
delF = 0.0;
log1 = 0.0;
log2 = 0.0;
sum_alphau = 0.0;
for (int s = 0; s < xK; s++) {
sum_alphau += xalphaut[s];
}
log2 -= xI*lgamma(xalphaut[kk]);
delF += xI*(boost::math::digamma(sum_alphau)- boost::math::digamma(xalphaut[kk]));
log2 += xI*lgamma(sum_alphau);
for (int i = 0; i < xI; i++) {
lp1 = 0;
for (int k = 0; k < xK; k++) {
if (xgammat(i,k) == 1) { lp1 +=1;}
}
lp0 = xK-lp1;
int p1[lp1]; flag1 = 0;
int p0[lp0]; flag0 = 0;
flagkk = 0; // whether gamma_k = 1
for (int k= 0; k < xK; k++) {
if (xgammat(i,k) == 1) {
p1[flag1] = k;
flag1 += 1;
if (k == kk) {flagkk = 1;}
} else {
p0[flag0] = k;
flag0 +=1;
}
}
if (flagkk==1) {
log2 += lgamma(xn_u(i,kk)+xalphaut[kk]);
delF +=boost::math::digamma(xn_u(i,kk)+xalphaut[kk]);
sum_nualphau = 0.0;
sum_nusalphau = 0.0;
for (int k = 0; k<lp1; k++) {
sums = xn_u(i,p1[k])+xalphaut[p1[k]];
sum_nualphau += sums;
sum_nusalphau += (sums+xn_s(i,p1[k]));
}
log2 -=lgamma(sum_nualphau);
log2 += lgamma(sum_nusalphau+1);
delF -=boost::math::digamma(sum_nualphau);
delF += boost::math::digamma(sum_nusalphau+1);
for (int k= 0; k<lp0; k++) {
sum_nusalphau +=(xn_u(i,p0[k])+xalphaut[p0[k]]+xn_s(i,p0[k]));
}
delF -= boost::math::digamma(sum_nusalphau+1);
log2 -= lgamma(sum_nusalphau+1);
} else {
log2 += lgamma(xn_u(i,kk)+xalphaut[kk]+xn_s(i,kk));
delF += boost::math::digamma(xn_u(i,kk)+xalphaut[kk]+xn_s(i,kk));
sum_nusalphau = 0.0;
for ( int k = 0; k<xK; k++) {
sum_nusalphau +=xn_u(i,k)+xalphaut[k]+xn_s(i,k);
}
log2 -= lgamma(sum_nusalphau+1);
delF -= boost::math::digamma(sum_nusalphau+1);
}
}
double mean_p = std::max(0.01, xalphaut[kk]+delF/xtt);
Rcpp::NumericVector alpha_u_p = Rcpp::rnorm(1, mean_p, sqrt_var[kk]);
if (alpha_u_p[0]>0.0) {
double alp[xK];
for (int i = 0; i<xK; i++) {
alp[i] = xalphaut[i];
}
alp[kk] = alpha_u_p[0];
log2 += log(gsl_ran_gaussian_pdf(alp[kk]-mean_p, sqrt_var[kk]));
delF = 0.0; sum_alphau = 0.0;
for (int s = 0; s < xK; s++) {
sum_alphau += alp[s];
}
log1 -= xI*lgamma(alp[kk]);
delF += xI*(boost::math::digamma(sum_alphau)- boost::math::digamma(alp[kk]));
log1 += xI*lgamma(sum_alphau);
for (int i = 0; i < xI; i++ ){
lp1 = 0;
for (int k = 0; k < xK; k++) {
if (xgammat(i,k) == 1) { lp1 +=1;}
}
lp0 = xK-lp1;
int p1[lp1]; flag1 = 0;
int p0[lp0]; flag0 = 0;
flagkk = 0; // whether gamma_k = 1
for (int k= 0; k < xK; k++) {
if (xgammat(i,k) == 1) {
p1[flag1] = k;
flag1 += 1;
if (k == kk) {flagkk = 1;}
} else {
p0[flag0] = k;
flag0 +=1;
}
}
if (flagkk==1) {
log1 += lgamma(xn_u(i,kk)+alp[kk]);
delF +=boost::math::digamma(xn_u(i,kk)+alp[kk]);
sum_nualphau = 0.0;
sum_nusalphau = 0.0;
for (int k = 0; k<lp1; k++) {
sums = xn_u(i,p1[k])+alp[p1[k]];
sum_nualphau += sums;
sum_nusalphau += (sums+xn_s(i,p1[k]));
}
log1 -=lgamma(sum_nualphau);
log1 += lgamma(sum_nusalphau+1);
delF -=boost::math::digamma(sum_nualphau);
delF += boost::math::digamma(sum_nusalphau+1);
for (int k= 0; k<lp0; k++) {
sum_nusalphau +=(xn_u(i,p0[k])+alp[p0[k]]+xn_s(i,p0[k]));
}
delF -= boost::math::digamma(sum_nusalphau+1);
log1 -= lgamma(sum_nusalphau+1);
} else {
log1 += lgamma(xn_u(i,kk)+alp[kk]+xn_s(i,kk));
delF += boost::math::digamma(xn_u(i,kk)+alp[kk]+xn_s(i,kk));
sum_nusalphau = 0.0;
for ( int k = 0; k<xK; k++) {
sum_nusalphau +=xn_u(i,k)+alp[k]+xn_s(i,k);
}
log1 -= lgamma(sum_nusalphau+1);
delF -=boost::math::digamma(sum_nusalphau+1);
}
}
mean_p = std::max(0.01, alp[kk] + delF/xtt);
log1 +=log(gsl_ran_gaussian_pdf(xalphaut[kk]-mean_p, sqrt_var[kk]));
log1 += log(gsl_ran_exponential_pdf(alp[kk],xlambda_u[kk])); //exponential prior
log2 += log(gsl_ran_exponential_pdf(xalphaut[kk],xlambda_u[kk])); //exponential prior
//if (alp[kk]<0 || alp[kk]>xlambda_u[kk]) {log1+=log(0);} //Uniform prior
//if (xalphaut[kk]<0 || xalphaut[kk]>xlambda_u[kk]) {log2+=log(0);} //Uniform prior
if (log(Rcpp::as<double>(Rcpp::runif(1)) ) <= (log1 - log2)) {
xalphaut[kk] = alp[kk];
xAalphau[kk] = 1;
} else{
xAalphau[kk] = 0;
}
}
}
return Rcpp::List::create(Rcpp::Named("alphau_tt") = xalphaut, Rcpp::Named("Aalphau") = xAalphau);
END_RCPP
}
