RcppDist (version 0.1.1)

bayeslm: bayeslm

Description

Demonstrates the use of RcppDist in C++ with Bayesian linear regression

Usage

bayeslm(y, x, iters = 1000L)

Value

A list of length two; the first element is a numeric matrix of the beta draws and the second element is a numeric vector of the sigma draws

Arguments

y

A numeric vector -- the response

x

A numeric matrix -- the explanatory variables; note this assumes you have included a column of ones if you intend there to be an intercept.

iters

An integer vector of length one, the number of posterior draws desired; the default is 1000.

Details

To see an example of using RcppDist C++ functions in C++ code, we can code up a Bayesian linear regression with completely uninformative priors (such that estimates should be equivalent to classical estimates). The code to do so is as follows:

#include <RcppDist.h>
// or, alternatively,
// #include <RcppArmadillo.h>
// #include <mvnorm.h>
// [[Rcpp::depends(RcppArmadillo, RcppDist)]]
// [[Rcpp::export]]
Rcpp::List bayeslm(const arma::vec& y, const arma::mat x,
                   const int iters = 1000) {
    int n = x.n_rows;
    int p = x.n_cols;
    double a = (n - p) / 2.0;
    arma::mat xtx = x.t() * x;
    arma::mat xtxinv = xtx.i();
    arma::vec mu = xtxinv * x.t() * y;
    arma::mat px = x * xtxinv * x.t();
    double ssq = arma::as_scalar(y.t() * (arma::eye(n, n) - px) * y);
    ssq *= (1.0 / (n - p));
    double b = 1.0 / (a * ssq);
    arma::mat beta_draws(iters, p);
    Rcpp::NumericVector sigma_draws(iters);
    for ( int iter = 0; iter < iters; ++iter ) {
        double sigmasq = 1.0 / R::rgamma(a, b);
        sigma_draws[iter] = sigmasq;
        // Here we can use our multivariate normal generator
        beta_draws.row(iter) = rmvnorm(1, mu, xtxinv * sigmasq);
    }
    return Rcpp::List::create(Rcpp::_["beta_draws"] = beta_draws,
                              Rcpp::_["sigma_draws"] = sigma_draws);
}

Examples

set.seed(123)
n <- 30
x <- cbind(1, matrix(rnorm(n*3), ncol = 3))
beta <- matrix(c(10, 2, -1, 3), nrow = 4)
y <- x %*% beta + rnorm(n)
freqmod <- lm(y ~ x[ , -1])
bayesmod <- bayeslm(y, x)
round(unname(coef(freqmod)), 2)
round(apply(bayesmod$beta_draws, 2, mean), 2)
c(beta)