##### https://github.com/cran/RcppDist
Tip revision: 284f865
bayeslm.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{bayeslm}
\alias{bayeslm}
\title{bayeslm}
\usage{
bayeslm(y, x, iters = 1000L)
}
\arguments{
\item{y}{A numeric vector -- the response}

\item{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.}

\item{iters}{An integer vector of length one, the number of posterior draws
desired; the default is 1000.}
}
\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
}
\description{
Demonstrates the use of RcppDist in C++ with Bayesian linear regression
}
\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:
\preformatted{
#include <RcppDist.h>
// or, alternatively,
// #include <mvnorm.h>

// [[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)
}