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Rcpp_Intro.cpp
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Rcpp_Intro.cpp
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// #include <Rcpp.h>
#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]
using namespace Rcpp;
// [[Rcpp::export]]
NumericVector convolveC(const NumericVector &a,
const NumericVector &b) {
// init
int N_a = a.size(), N_b = b.size(), N_ab = N_a + N_b - 1;
NumericVector ab(N_ab);
for (int i=0; i<N_a; ++i)
for (int j=0; j<N_b; ++j) {
ab[i+j] += a[i] * b[j];
}
return ab;
}
// [[Rcpp::export]]
NumericVector bootstrapC(const NumericVector &ds, const int B = 1000) {
NumericMatrix boot_stat(B, 2);
int n = ds.size();
for (int i=0; i<B; ++i) {
NumericVector smpl = ds[floor(runif(n, 0, n))];
boot_stat(i, 0) = mean(smpl);
boot_stat(i, 1) = sd(smpl);
}
return boot_stat;
}
// [[Rcpp::export]]
arma::mat rmvnorm(int n,
const arma::vec &mu,
const arma::mat &sigma) {
// multivariate normal distribution
unsigned int p = sigma.n_cols;
NumericVector draw = rnorm(n*p);
// reshape in place, copy_aux_mem = false
arma::mat Z = arma::mat(draw.begin(), n, p, false, true);
// repreat mu from (p,) to (n, p)
// Cholesky decomposition of variance-covariance matrix
// an algorithm to compute matrix version of sqrt()
arma::mat Y = arma::repmat(mu, 1, n).t() + Z * arma::chol(sigma);
return Y;
}
// [[Rcpp::export]]
List fastLM(const arma::mat &X,
const arma::colvec &y) {
// Linear model
// # of obs. and regressors
int n = X.n_rows, k = X.n_cols;
arma::colvec coef = (X.t() * X).i() * X.t() * y;
arma::colvec res = y - X*coef;
double s2 = std::inner_product(res.begin(), res.end(), res.begin(), 0.) / (n-k);
arma::colvec std_err = arma::sqrt(s2 * arma::diagvec(arma::pinv(X.t()*X)));
return List::create(
_["coefficients"] = coef,
_["stderr"] = std_err,
_["df.residual"] = res
);
}