Bootstrap a Regression Model, the Bayesian Way

sbayesboot() performs a Bayesian bootstrap of a regression model.

Usage

sbayesboot(object, reps = 1000L, seed, cluster = NULL, ...)

Arguments

object: a regression model object
reps: how many bootstrap replicates the user wants. Defaults to 1000
seed: set an optional seed for reproducibility
cluster: an optional cluster for calibrating the weights
...: optional arguments

Value

sbayesboot() takes a fitted regression model and returns a matrix of bootstrapped coefficients (with intercept). These could be easily converted to a data frame for ease of summary.

Details

The code underpinning sbayesboot() is largely derived from code provided by Grant McDermott and Vincent Arel-Bundock. My approach here takes the flexibility of McDermott's model-agnostic code (along with the ease of specifying clusters) and combines it with Arel-Bundock's update() approach to the actual bootstrapping. I may have screwed something up, so feel free to point to cases where I did screw up.

Author

Grant McDermott, Vincent Arel-Bundock

Examples

# \donttest{
M1 <- lm(mpg ~ disp + wt + hp, mtcars)

# Default options

BB1 <- sbayesboot(M1)

# Cluster bootstrap on cylinder variable

BB2 <- sbayesboot(M1, cluster=~cyl)
# }