3 Matching Annotations
  1. Jul 2019
    1. The Freedman-Diaconis rule is very robust and works well in practice. The bin-width is set to h=2×IQR×n−1/3h=2×IQR×n−1/3h=2\times\text{IQR}\times n^{-1/3}. So the number of bins is (max−min)/h(max−min)/h(\max-\min)/h, where nnn is the number of observations, max is the maximum value and min is the minimum value.

      How to determine the number of bins to use in a histogram.

  2. Jun 2019
    1. int.hist = function(x,ylab="Frequency",...) { barplot(table(factor(x,levels=min(x):max(x))),space=0,xaxt="n",ylab=ylab,...);axis(1) }