findSubsample: Finding an Optimized Subsample

Description

It allows one to find an optimized (minimized or maximized) numeric subsample according to a statistic of interest. For example, it might be of interest to determine a subsample whose standard deviation is the lowest among all of those obtained from all possible subsamples of the same size.

Usage

findSubsample(x, size, fun = sd, minimize = TRUE, niter = 10000)

Value

A list of

dataname: a character.
niter: the number of iterations.
fun: the objective function.
stat: the achieved statistic for the optimized subsample.
criterion: a character indicating the type of optimization.
subsample: a numeric vector; the optimized subsample.
labels: a string containg the labels of the subsample values.

Arguments

x: a numeric vector.
size: an integer; the size of the subsample.
fun: an object of class function; the statistic at which to evaluate the subsample.
minimize: logical; if TRUE (default) findSubsample will find a subsample that minimizes stat.
niter: an integer indicating the number of iterations, i.e., the number of subsamples to be selected (without replacement) from the original sample, x. The larger is this number, the more optimized is the subsample to be found, but this also implies in time-consuming.

Author

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Examples

Run this code

# Example 1
y <- rnorm(40, 5, 2)
findSubsample(x = y, size = 6)

# Example 2
f <- function(x) diff(range(x)) # max(x) - min(x)
findSubsample(x = y, size = 6, fun = f, minimize = FALSE, niter = 20000)

# End (not run)