const ArrayXd glmDist::devResid(const ArrayXd &y, const ArrayXd &mu, const ArrayXd &wt) const {
int n = mu.size();
return as<ArrayXd>(::Rf_eval(::Rf_lang4(as<SEXP>(d_devRes),
as<SEXP>(NumericVector(y.data(), y.data() + n)),
as<SEXP>(NumericVector(mu.data(), mu.data() + n)),
as<SEXP>(NumericVector(wt.data(), wt.data() + n))
), d_rho));
}
double glmDist::aic(const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu,
const ArrayXd& wt, double dev) const {
int nn = mu.size();
double ans =
::Rf_asReal(::Rf_eval(::Rf_lang6(as<SEXP>(d_aic),
as<SEXP>(NumericVector(y.data(), y.data() + nn)),
as<SEXP>(NumericVector(n.data(), n.data() + nn)),
as<SEXP>(NumericVector(mu.data(), mu.data() + nn)),
as<SEXP>(NumericVector(wt.data(), wt.data() + nn)),
PROTECT(::Rf_ScalarReal(dev))), d_rho));
UNPROTECT(1);
return ans;
}
// Returns the linear predictor from the full step
Rcpp::NumericVector
densePred::gamma(const Rcpp::NumericVector& xwts,
const Rcpp::NumericVector& wtres)
throw (std::runtime_error) {
int n = d_X.nrow();
if (xwts.size() != n || wtres.size() != n)
throw
std::runtime_error("length(xwts) or length(wtres) != nrow(X)");
arma::vec a_xwts = arma::vec(xwts.begin(), n, false, true),
a_wtres = arma::vec(wtres.begin(), n, false, true);
arma::vec tmp = solve(diagmat(a_xwts) * a_X, a_wtres);
std::copy(tmp.begin(), tmp.end(), a_delta.begin());
d_coef = d_coef0 + d_delta;
return NumericVector(wrap(a_X * a_coef));
}
// Returns the linear predictor from a step of fac
Rcpp::NumericVector densePred::dgamma(double fac) {
return NumericVector(wrap(a_X * (a_coef0 + fac * a_delta)));
}
AdaGradIter::AdaGradIter(GloveFit &fit):
x_irow(IntegerVector(0)),
x_icol(IntegerVector(0)),
x_val(NumericVector(0)),
iter_order(IntegerVector(0)),
fit(fit) {};
Rcpp::NumericVector merResp::devResid() const {
return NumericVector(0);
}
