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This package uses Rcpp to connect the R system to the GNU GSL, a collection of numerical routines for scientific computing, particularly its vector and matrix classes.
lm() for OLS
regressionThe fastLm() function included
as file src/fastLm.cpp in the package:
#include <RcppGSL.h>
#include <gsl/gsl_multifit.h>
#include <cmath>
// [[Rcpp::export]]
Rcpp::List fastLm(const RcppGSL::Matrix &X, const RcppGSL::Vector &y) {
int n = X.nrow(), k = X.ncol();
double chisq;
RcppGSL::Vector coef(k); // to hold the coefficient vector
RcppGSL::Matrix cov(k,k); // and the covariance matrix
// the actual fit requires working memory we allocate and free
gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc (n, k);
gsl_multifit_linear (X, y, coef, cov, &chisq, work);
gsl_multifit_linear_free (work);
// assign diagonal to a vector, then take square roots to get std.error
Rcpp::NumericVector std_err;
std_err = gsl_matrix_diagonal(cov); // need two step decl. and assignment
std_err = Rcpp::sqrt(std_err); // sqrt() is an Rcpp sugar function
return Rcpp::List::create(Rcpp::Named("coefficients") = coef,
Rcpp::Named("stderr") = std_err,
Rcpp::Named("df.residual") = n - k);
}This example comes from the complete
example package included in RcppGSL and is from the
file inst/examples/RcppGSLExample/src/colNorm.cpp
#include <RcppGSL.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_blas.h>
// [[Rcpp::export]]
Rcpp::NumericVector colNorm(const RcppGSL::Matrix & G) {
int k = G.ncol();
Rcpp::NumericVector n(k); // to store results
for (int j = 0; j < k; j++) {
RcppGSL::VectorView colview = gsl_matrix_const_column (G, j);
n[j] = gsl_blas_dnrm2(colview);
}
return n; // return vector
}On CRAN and here.
Dirk Eddelbuettel and Romain Francois
GPL (>= 2)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.