partition_factor creates a represampling object, i.e. a set of sample indices defining cross-validation test and training sets.

partition_factor(
data,
coords = c("x", "y"),
fac,
return_factor = FALSE,
repetition = 1
)

## 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. either the name of a variable (column) in data, or a vector of type factor and length nrow(data) that contains the partitions to be used for defining training and test samples. if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value) 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.

## Note

In this partitioning approach, all repetitions are identical and therefore pseudo-replications.

## Examples

data(ecuador)
# I don't recommend using this partitioning for cross-validation,
# this is only for demonstration purposes:
breaks <- quantile(ecuador$dem, seq(0, 1, length = 6)) ecuador$zclass <- cut(ecuador$dem, breaks, include.lowest = TRUE) summary(ecuador$zclass)
#> [1.72e+03,1.92e+03] (1.92e+03,2.14e+03] (2.14e+03,2.31e+03] (2.31e+03,2.57e+03]
#>                 151                 150                 150                 150
#> (2.57e+03,3.11e+03]
#>                 150 parti <- partition_factor(ecuador, fac = "zclass")
#> \$1
#>