bootruin (version 1.2-4)

rpdataboot: Creating Bootstrap Replications from an Matrix of Observations

Description

This function provides a simple way to create bootstrap replications of a dataset. The replication is either non-parametrical or parametrical (for exponential or logarithmic normal data).

Usage

rpdataboot(x, b, method = c("nonp", "exp", "lnorm"))

Arguments

x

A matrix containing numeric values

b

The number of bootstrap replications

method

The replication method applied to the data

Value

An array of dimension c(dim(x), b) containing column-wise bootstrap replications of x

Details

The input matrix x is supposed to contain (independent) observations in each column. The bootstrap replication take this into account is done column-wise.

Depending on how the boostrap replications are further processed, the boostrap resampling should be done either non-parametrically (method = "nonp") or parametrically.

In the non-parametrical case, the bootstrap replications are samples drawn from the empirical distribution of the original observation, this is equivalent to drawing with replacement.

For the parametrical bootstrap replications there are currently two options: With method = "exp" each bootstrap replication is a vector simulated from an exponential distribution function whose parameter is estimated by the original observation. For method = "lnorm" the resampling is done by simulating from a logarithmic normal distribution whose log-mean and log-variance are estimated from the original observation.

See Also

rpdataconv for creating a suitable data matrix from a list of observation vectors, and rpdatasim for creating such a matrix by simulation.

Examples

# Generate a data matrix of 5 samples with 10 observations each.
x <- matrix(rexp(50), nrow = 10, ncol = 5)

# Create (parametric) bootstrap replications
x.boot.par <- bootruin:::rpdataboot(x, b = 50, method = "exp")

# Create (non-parametric) bootstrap replications
x.boot.nonp <- bootruin:::rpdataboot(x, b = 50, method = "nonp")