`R/sperrorest_resampling.R`

`partition_factor_cv.Rd`

`partition_factor_cv`

creates a represampling object, i.e. a
set of sample indices defining cross-validation test and training sets,
where partitions are obtained by resampling at the level of groups of
observations as defined by a given factor variable. This can be used, for
example, to resample agricultural data that is grouped by fields, at the
agricultural field level in order to preserve spatial autocorrelation
within fields.

```
partition_factor_cv(
data,
coords = c("x", "y"),
fac,
nfold = 10,
repetition = 1,
seed1 = NULL,
return_factor = FALSE
)
```

- data
`data.frame`

containing at least the columns specified by`coords`

- coords
vector of length 2 defining the variables in

`data`

that contain the x and y coordinates of sample locations.- fac
either the name of a variable (column) in

`data`

, or a vector of type factor and length`nrow(data)`

that defines groups or clusters of observations.- nfold
number of partitions (folds) in

`nfold`

-fold cross-validation partitioning- repetition
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
`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.- return_factor
if

`FALSE`

(default), return a represampling object; if`TRUE`

(used internally by other sperrorest functions), return a`list`

containing factor vectors (see Value)

A represampling object, see also partition_cv for details.

In this partitioning approach, the number of factor levels in `fac`

must be large enough for this factor-level resampling to make sense.