**Publishable Stuff**, and kindly contributed to R-bloggers)

I recently wrapped up a version of my R function for easy Bayesian bootstrappin’ into the package `bayesboot`

. This package implements a function, also named `bayesboot`

, which performs the Bayesian bootstrap introduced by Rubin in 1981. The Bayesian bootstrap can be seen as a *smoother* version of the classical non-parametric bootstrap, but I prefer seeing the classical bootstrap as *an approximation* to the Bayesian bootstrap 🙂

The implementation in `bayesboot`

can handle both summary statistics that works on a weighted version of the data (such as `weighted.mean`

) and that works on a resampled data set (like `median`

). As `bayesboot`

just got accepted on CRAN you can install it in the usual way:

`install.packages("bayesboot")`

You’ll find the source code for

`bayesboot`

on GitHub.

If you want to know more about the model behind the Bayesian bootstrap you can check out my previous blog post on the subject and, of course, the original paper by Rubin (1981).## A simple example of

`bayesboot`

in actionAs in a previous post on the Bayesian bootstrap, here is again a Bayesian bootstrap analysis of the mean height of American presidents using the heights of the last ten presidents:

# Heights of the last ten American presidents in cm (Kennedy to Obama). heights <- c(183, 192, 182, 183, 177, 185, 188, 188, 182, 185)

The

`bayesboot`

function needs, at least, a vector of data and a function implementing a summary statistic. Here we have the data`height`

and we’re going with the sample`mean`

as our summary statistic:

`library(bayesboot) b1 <- bayesboot(heights, mean)`

The resulting posterior distribution over probable mean heights can now be

`plot`

ted and`summary`

ized:

summary(b1)

## Bayesian bootstrap ## ## Number of posterior draws: 4000 ## ## Summary of the posterior (with 95% Highest Density Intervals): ## statistic mean sd hdi.low hdi.high ## V1 184.5 1.181 182.1 186.8 ## ## Quantiles: ## statistic q2.5% q25% median q75% q97.5% ## V1 182.2 183.7 184.5 185.3 186.9 ## ## Call: ## bayesboot(data = heights, statistic = mean)

plot(b1)

A shout-out to Mike Meredith and John Kruschke who implemented the great BEST and HDInterval packages which

`summary`

and`plot`

utilizes. Note here that thepoint meanin the summary and plot above refers to the mean of the posterior distribution andnotthe sample mean of any presidents.While it is possible to use a summary statistic that works on a resample of the original data, it is more efficient to use a summary statistic that works on a

reweightingof the original dataset. So instead of using`mean`

as above it would be better to use`weighted.mean`

, like this:

b2 <- bayesboot(heights, weighted.mean, use.weights = TRUE)

The result will be almost the same as before, but the above will be somewhat faster to compute.

The result of a call to

`bayesboot`

will always result in a`data.frame`

with one column per dimension of the summary statistic. If the summary statistic does not return a named vector the columns will be called`V1`

,`V2`

, etc. The result of a`bayesboot`

call can be further inspected and post processed. For example:

# Given the model and the data, this is the probability that the mean # heights of American presidents is above the mean heights of # American males as given by www.cdc.gov/nchs/data/series/sr_11/sr11_252.pdf mean( c(b2$V1 > 175.9, TRUE, FALSE) )

`## [1] 0.9998`

## Comparing two groups

If we want to compare the means of two groups, we will have to call

`bayesboot`

twice with each dataset and then use the resulting samples to calculate the posterior difference. For example, let’s say we have the heights of the opponents that lost to the presidents in`height`

the first time those presidents were elected. Now we are interested in comparing the mean height of American presidents with the mean height of presidential candidates that lost.

# The heights of opponents of American presidents (first time they were elected). # From Richard Nixon to John McCain heights_opponents <- c(182, 180, 180, 183, 177, 173, 188, 185, 175) # Running the Bayesian bootstrap for both datasets b_presidents <- bayesboot(heights, weighted.mean, use.weights = TRUE) b_opponents <- bayesboot(heights_opponents, weighted.mean, use.weights = TRUE) # Calculating the posterior difference and converting back to a # bayesboot object for pretty plotting. b_diff <- as.bayesboot(b_presidents - b_opponents) plot(b_diff)

So there is some evidence that winning presidents are a couple of cm taller than loosing opponents. (Though, I must add that it is quite unclear what the purpose really is of analyzing the heights of presidents and opponents…)

## More information

The README and documentation of

`bayesboot`

contains more examples. If you find any bugs or have suggestions for improvements consider submitting an issue on GitHub.## References

Rubin, D. B. (1981). The Bayesian bootstrap.

The annals of statistics, 9(1), 130–134. link to paper

Toleave a commentfor the author, please follow the link and comment on their blog:Publishable Stuff.

R-bloggers.com offersdaily e-mail updatesabout R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...