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Entropy and Testing with np

Source: vignettes/np_entropy_tests.Rmd
np_entropy_tests.Rmd

This vignette is meant to be a small package-side introduction to the entropy-based testing tools in np. It is intentionally much shorter than the legacy article-style document and focuses on what the functions are for and one small runnable example.

The fuller narrative treatment now belongs on the gallery site rather than in a shipped package vignette:

Main functions

The main entropy-based testing functions are:

  • npdeneqtest: equality of multivariate densities
  • npunitest: equality of univariate densities
  • npsymtest: asymmetry in a univariate variable or series
  • npdeptest: nonlinear pairwise dependence
  • npsdeptest: nonlinear serial dependence

These functions can be computationally demanding, especially when integration and bootstrap resampling are involved.

A small example

For a first run, it is reasonable to begin with a simple univariate comparison and keep the example small enough that bootstrapping remains practical.

library(np)
#> np 0.70-4
#> Examples and guides at https://jeffreyracine.github.io/gallery/
#> See also vignette("np_getting_started", package = "np")
set.seed(42)

n <- 250
x <- rnorm(n)
y <- rnorm(n)

npunitest(x, y, bootstrap = TRUE)
#> 
#> Consistent Univariate Entropy Density Equality Test
#> 399 Bootstrap Replications
#> 
#> Test Statistic 'Srho': 0.001213156   P Value: 0.98997  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Fail to reject the null of equality at the 10% level

Practical guidance

  • start with the smallest example that answers your question,
  • use the default integral-based versions for serious work unless you have a reason not to,
  • expect runtime to grow quickly when bootstrap resampling is involved,
  • if the testing workflow is correct but the runtime becomes burdensome, move to npRmpi rather than rewriting the statistical problem.

Where to go next