represampling_bootstrap draws a bootstrap random sample (with replacement) from data.

represampling_bootstrap(
data,
coords = c("x", "y"),
nboot = nrow(data),
repetition = 1,
seed1 = NULL,
oob = FALSE
)

## Arguments

data data.frame containing at least the columns specified by coords vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations. Size of bootstrap sample numeric vector: cross-validation repetitions to be generated. Note that this is not the number of repetitions, but the indices of these repetitions. E.g., use repetition = c(1:100) to obtain (the 'first') 100 repetitions, and repetition = c(101:200) to obtain a different set of 100 repetitions. seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set. logical (default FALSE): if TRUE, use the out-of-bag sample as the test sample; if FALSE, draw a second bootstrap sample of size nboot independently to obtain a test sample.

## Value

A represampling object. This is a (named) list containing length(repetition). resampling objects. Each of these contains only one list with indices of training and test samples. Indices are row indices for data.

## Examples

data(ecuador)
# only 10 bootstrap repetitions, normally use >=100:
parti <- represampling_bootstrap(ecuador, repetition = 10)
# plot(parti, ecuador) # careful: overplotting occurs
# because some samples are included in both the training and
# the test sample (possibly even multiple times)