Finding an Optimized Subsample

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.

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

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.

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.

Author

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

See also

sample, creategroups

Examples

# Example 1
y <- rnorm(40, 5, 2)
findSubsample(x = y, size = 6)
#> 
#> 	Finding an Optimized Subsample
#> 
#> Data: y 
#> Iterations: 10000 
#> Objective function: sd = 0.4062577 ( minimized ) 
#> Subsample: 
#>       30       32       40       33       16       14 
#> 5.740864 5.202569 6.211756 5.078155 5.478129 5.485777 

# Example 2
f <- function(x) diff(range(x)) # max(x) - min(x)
findSubsample(x = y, size = 6, fun = f, minimize = FALSE, niter = 20000)
#> 
#> 	Finding an Optimized Subsample
#> 
#> Data: y 
#> Iterations: 20000 
#> Objective function: f = 11.20151 ( maximized ) 
#> Subsample: 
#>        31        10        13        35         5        27 
#>  4.034646  5.969443  7.888373 -2.419699  8.630062  8.781806 

# End (not run)