# Robust Estimators of Location and Scale

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First, the `median_Rcpp`

function is defined to compute the median of the given input vector. It is assumed that the input vector is unsorted, so a copy of the input vector is made using `clone`

and then `std::nth_element`

is used to access the `nth`

sorted element. Since we only care about accessing one sorted element of the vector for an odd length vector and two sorted elements for an even length vector, it is faster to use `std::nth_element`

than either `std::sort`

or `std::partial_sort`

.

#includeusing namespace Rcpp; // [[Rcpp::export]] double median_rcpp(NumericVector x) { NumericVector y = clone(x); int n, half; double y1, y2; n = y.size(); half = n / 2; if(n % 2 == 1) { // median for odd length vector std::nth_element(y.begin(), y.begin()+half, y.end()); return y[half]; } else { // median for even length vector std::nth_element(y.begin(), y.begin()+half, y.end()); y1 = y[half]; std::nth_element(y.begin(), y.begin()+half-1, y.begin()+half); y2 = y[half-1]; return (y1 + y2) / 2.0; } } library(rbenchmark) set.seed(123) z <- rnorm(1000000) median_rcpp(1:10) [1] 5.5 median_rcpp(1:9) [1] 5 # benchmark median_rcpp and median benchmark(median_rcpp(z), median(z), order="relative")[,1:4] test replications elapsed relative 1 median_rcpp(z) 100 1.747 1.000 2 median(z) 100 5.991 3.429

Next, the `mad_rcpp`

function is defined to compute the median absolute deviation. This is a simple one-liner thanks to the sugar function `abs`

, the vectorized operators, and the `median_rcpp`

function defined above. Note that a default value is specified for the scale_factor argument.

// [[Rcpp::export]] double mad_rcpp(NumericVector x, double scale_factor = 1.4826) { // scale_factor = 1.4826; default for normal distribution consistent with R return median_rcpp(abs(x - median_rcpp(x))) * scale_factor; } # benchmark mad_rcpp and mad benchmark(mad_rcpp(z), mad(z), order="relative")[,1:4] test replications elapsed relative 1 mad_rcpp(z) 100 3.678 1.000 2 mad(z) 100 13.443 3.655

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