represampling_disc_bootstrap performs a spatial block bootstrap by resampling at the level of rectangular partitions or 'tiles' generated by partition_tiles.

represampling_disc_bootstrap(
data,
coords = c("x", "y"),
nboot,
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. number of bootstrap samples; you may specify different values for the training sample (nboot[1]) and for the test sample (nboot[2]). 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 (the complement of the nboot[1] test set discs, minus the buffer area as specified in the ... arguments to partition_disc); if FALSE, draw a second bootstrap sample of size nboot independently to obtain a test sample (sets of overlapping discs drawn with replacement). additional arguments to be passed to partition_disc; note that a buffer argument has not effect if oob=FALSE; see example below

## Note

Performs nboot out of nrow(data) resampling of circular discs. This is an overlapping spatial block bootstrap where the blocks are circular.

## Examples

data(ecuador)
# Overlapping disc bootstrap:
radius = 200, nboot = 20,
oob = FALSE
)