R/sperrorest_resampling.R
partition_factor.Rdpartition_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
)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.
A represampling object, see also partition_cv for details.
In this partitioning approach, all repetitions are identical and
therefore pseudo-replications.
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")
# plot(parti,ecuador)
summary(parti)
#> $`1`
#> n.train n.test
#> [1.72e+03,1.92e+03] 600 151
#> (1.92e+03,2.14e+03] 601 150
#> (2.14e+03,2.31e+03] 601 150
#> (2.31e+03,2.57e+03] 601 150
#> (2.57e+03,3.11e+03] 601 150
#>