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doBootstrap

update 2016-11-06: I just chanced across a sequence of blogposts one, two, and three detailing Bayesian and classical non-parametric bootstrap, and detailing a package ('bayesboot', on CRAN) for Bayesian bootstrapping. I haven't checked it out yet. (My implmentation below is a simple, "classical" non-parametric bootstrap.)

This repository contains some basic functions, written in R, to bootstrap effect sizes and confidence intervals for descriptive statistics, regressions, etc.

  • [doBootstrapPrimer.pdf] (../../raw/master/doBootstrapPrimer.pdf) : contains a primer on bootstrapping.
  • doBootstrapReadme.R : contains a few examples for using doBootstrap.R
  • doBootstrap.R : code.

The code is slow as I have not optimized/did not use R's optimized boot functions. That will hopefully be fixed in a future version.

Currently doBootstrap supports:

  • Single-sample descriptive statistics: mean
  • Two-sample descriptive statistics: correlations, differences (paired and unpaired), cohen's d (paired and unpaired)
  • (Simple) Mediation (using code written by Benoit Monin)
  • Custom written functions (or others that are not specified, e.g. median)
  • Fixed effect multiple linear regression (using lm)
  • Mixed effect multiple linear regression (using lme4::lmer)

On the todo list:

  • add generalized linear models (glms)
  • add moderated mediation / mediated moderation
  • add learning curve
  • allow a "generalized" version (like R's boot), to pass in own function.
Changelog
  • Aug 2014: Added a rough draft of a bootstrap primer. Added functionality to doBoot to accept custom functions.
  • Jan 2014: First basic version. means, correlations, differences, linear (fixed and mixed) regressions.