findSubsample: Finding an Optimized Subsample
In biotools: Tools for Biometry and Applied Statistics in Agricultural Science

Description Usage Arguments Value Author(s) See Also Examples

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.

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

`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.

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.

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

sample, creategroups

# 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)