int main()
{
double a = Rf_dnorm4(0, 0, 1, 0);
printf("%f", a);
return 0;
}
double Clmbr::prden( double xi, double * err)
// integrand for sl( theta, alpha ) generic formula
{
double den;
if(variance_unknown) { if (th0ex) den= fk(m,xi); else den= fk(m-1,xi); }
else den = Rf_dnorm4(lambda*xi ,0,1,0) ;
double z_tilde;
if( th0ex ) z_tilde = 0; else z_tilde = xi*c1 + c2;
const double deltasq = lambdasq*(1-xi*xi) + z_tilde*z_tilde;
if(variance_unknown) z= z_tilde/sqrt(deltasq); else z= z_tilde;
double wsq;
if(variance_unknown) wsq = 1 - omega/deltasq; else wsq = deltasq - omega;
w = sqrt(max(wsq,0.));
double er= 0, *const per= &er;
const double pr = sl_geo(per);
if( err != 0 ) *err += (*per)*den;
return pr*den;
}
// Posterior Density Function to sample Theta
double f_theta( colvec logTheta, colvec mTheta, mat Otheta, double Tau, mat Y, mat Fit, rowvec Sigma, double logEHRTime )
{
double prior, like, post;
colvec cTheta = ( logTheta - mTheta ) ;
prior = - 0.5 * Tau * as_scalar( cTheta.t() * Otheta * cTheta );
mat D = diagmat( 1 / Sigma );
like = - 0.5 * accu( pow ( log( Y ) - log( Fit ), 2 ) * D ); // Need to figure out what to do with Sigma
// Conditional Posterior -----------------------------------------------------------
post = prior + like + Rf_dnorm4( logEHRTime, 0, 1, 1 );
return post;
}
void normden(double *x, double *mean, double *sd, int *log, double *a)
{
*a = Rf_dnorm4(*x, *mean, *sd, *log);
}
// [[register]]
RcppExport SEXP updatealphas_Exp(SEXP alphast,
SEXP n_s,
SEXP K,
SEXP I,
SEXP lambda_s,
SEXP gammat,
SEXP var_1,
SEXP var_2,
SEXP p_var,
SEXP ttt) {
BEGIN_RCPP
Rcpp::NumericVector xalphast(alphast);
Rcpp::IntegerMatrix xn_s(n_s);
Rcpp::IntegerMatrix xgammat(gammat);
int xI = Rcpp::as<int>(I);
int xK = Rcpp::as<int>(K);
Rcpp::NumericVector sqrt_var1(var_1);
Rcpp::NumericVector sqrt_var2(var_2);
int xtt = Rcpp::as<int>(ttt);
Rcpp::NumericVector xlambda_s(lambda_s);
Rcpp::IntegerVector xAalphas(xK);
Rcpp::RNGScope scope;
Rcpp::NumericVector xp_var(p_var); // proposal mixture
double delF = 0.0;
double psik = 0.;
double log1 = 0.0;
double log2 = 0.0;
double sums = 0.;
double sum_alp_ns = 0.0;
double sum_alp = 0.0;
double sum_gl_alp = 0.0;
double sum_gl_alp_ns = 0.0;
int flag1 = 0;
int flagkk = 0;
int lp1 = 0;
for (int kk = 0; kk < xK; kk++) {
delF = 0.0;
psik = digamma(xalphast[kk]);
log1 = 0.0;
log2 = 0.0;
for (int i = 0; i < xI; i++) {
lp1 = 0;
for (int k = 0; k < xK; k++) {
if (xgammat(i, k) == 1) {
lp1 += 1;
}
}
std::vector<int> p1(lp1);
flag1 = 0;
flagkk = 0;
for (int k = 0; k < xK; k++) {
if (xgammat(i, k) == 1) {
p1[flag1] = k;
flag1 += 1;
if (k == kk) {
flagkk = 1;
}
}
}
sum_alp_ns = 0.0;
sum_alp = 0.0;
sum_gl_alp = 0.0;
sum_gl_alp_ns = 0.0;
for (int k = 0; k < lp1; k++) {
sums = xalphast[p1[k]] + xn_s(i, p1[k]);
sum_alp_ns += sums;
sum_alp += xalphast[p1[k]];
sum_gl_alp += lgamma(xalphast[p1[k]]);
sum_gl_alp_ns += lgamma(sums);
}
if (flagkk > 0) {
delF += digamma(xn_s(i, kk) + xalphast[kk]) - psik -
digamma(sum_alp_ns) + digamma(sum_alp);
}
if (lp1 > 0) {
log2 += -(sum_gl_alp - lgamma(sum_alp)) +
(sum_gl_alp_ns - lgamma(sum_alp_ns));
}
}
double mean_p = std::max(0.01, xalphast[kk] + delF / xtt);
Rcpp::NumericVector alpha_s_p = Rcpp::rnorm(1, mean_p, sqrt_var1[kk]);
if (Rcpp::as<double>(Rcpp::rbinom(1, 1, xp_var[kk])) == 1) {
alpha_s_p = Rcpp::rnorm(1, mean_p, sqrt_var1[kk]);
} else {
alpha_s_p = Rcpp::rnorm(1, mean_p, sqrt_var2[kk]);
}
if (alpha_s_p[0] > 0.0) {
std::vector<double> alp(xK);
for (int i = 0; i < xK; i++) {
alp[i] = xalphast[i];
}
alp[kk] = alpha_s_p[0];
// log2 += log(xp_var[kk]*gsl_ran_gaussian_pdf(alp[kk]-mean_p,
// sqrt_var1[kk])+(1-xp_var[kk])*gsl_ran_gaussian_pdf(alp[kk]-mean_p,
// sqrt_var2[kk]));
log2 +=
log(xp_var[kk] * Rf_dnorm4(alp[kk], mean_p, sqrt_var1[kk], 0) +
(1 - xp_var[kk]) * Rf_dnorm4(alp[kk], mean_p, sqrt_var2[kk], 0));
delF = 0.0;
psik = digamma(alp[kk]);
for (int i = 0; i < xI; i++) {
lp1 = 0;
for (int k = 0; k < xK; k++) {
if (xgammat(i, k) == 1) {
lp1 += 1;
}
}
std::vector<int> p1(lp1);
flag1 = 0;
flagkk = 0;
for (int k = 0; k < xK; k++) {
if (xgammat(i, k) == 1) {
p1[flag1] = k;
flag1 += 1;
if (k == kk) {
flagkk = 1;
}
}
}
sum_alp_ns = 0.0;
sum_alp = 0.0;
sum_gl_alp = 0.0;
sum_gl_alp_ns = 0.0;
for (int k = 0; k < lp1; k++) {
sums = alp[p1[k]] + xn_s(i, p1[k]);
sum_alp_ns += sums;
sum_alp += alp[p1[k]];
sum_gl_alp += lgamma(alp[p1[k]]);
sum_gl_alp_ns += lgamma(sums);
}
if (flagkk > 0) {
delF += digamma(xn_s(i, kk) + xalphast[kk]) - psik -
digamma(sum_alp_ns) + digamma(sum_alp);
}
if (lp1 > 0) {
log1 += -(sum_gl_alp - lgamma(sum_alp)) +
(sum_gl_alp_ns - lgamma(sum_alp_ns));
}
}
mean_p = std::max(0.01, alp[kk] + delF / xtt);
// log1 +=log(xp_var[kk]*gsl_ran_gaussian_pdf(xalphast[kk]-mean_p,
// sqrt_var1[kk])+(1-xp_var[kk])*gsl_ran_gaussian_pdf(xalphast[kk]-mean_p,
// sqrt_var2[kk]));
log1 += log(
xp_var[kk] * Rf_dnorm4(xalphast[kk], mean_p, sqrt_var1[kk], 0) +
(1 - xp_var[kk]) * Rf_dnorm4(xalphast[kk], mean_p, sqrt_var2[kk], 0));
// log1 += log(gsl_ran_exponential_pdf(alp[kk],xlambda_s[kk]));
// //exponential prior
log1 += Rf_dexp(alp[kk], xlambda_s[kk], 1);
// log2 +=
// log(gsl_ran_exponential_pdf(xalphast[kk],xlambda_s[kk]));//exponential
// prior
log2 += Rf_dexp(xalphast[kk], xlambda_s[kk], 1);
// if (alp[kk]<0 || alp[kk]>xlambda_s[kk]) {log1+=log(0);} //Uniform prior
// if (xalphast[kk]<0 || xalphast[kk]>xlambda_s[kk]) {log2+=log(0);}
// //Uniform prior
if (log(Rcpp::as<double>(Rcpp::runif(1))) <= (log1 - log2)) {
xalphast[kk] = alp[kk];
xAalphas[kk] = 1;
} else {
xAalphas[kk] = 0;
}
}
}
return Rcpp::List::create(Rcpp::Named("alphas_tt") = xalphast,
Rcpp::Named("Aalphas") = xAalphas);
END_RCPP
}
