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

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



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.


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.


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)