# Statistics

## Classifying Blood Bowl teams using clustered heatmaps

Restate my assumptions: If you graph the numbers of any system, patterns emerge. In this post we'll use clustered heatmaps to graph the numbers from the Blood Bowl Fantasy football game, and see what patterns emerge!

## Optimal performance with Random Forests: does feature selection beat tuning?

This blog post demonstrates that the presence of irrelevant variables can reduce the performance of the Random Forest algorithm (as implemented in R by ranger()). The solution is either to tune one of the algorithm's parameters, OR to remove irrelevant features using a procedure called Recursive Feature Elimination (RFE).

## Using R to analyse the Roche Antigen Rapid Test: How accurate is it?

This blog post is about the Roche Rapid Antigen Test Nasal. How accurate is it? I tracked down the data mentioned in the kit's leaflet, discuss the whole measurement process and used R to reproduce the sensitivity and specificity of the test.

## Using posterior predictive distributions to get the Average Treatment Effect (ATE) with uncertainty

Here we show how to use Stan and the brms R-package to calculate the posterior predictive distribution of a covariate-adjusted average treatment effect (ATE).