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)
data |
|
---|---|
coords | vector of length 2 defining the variables in |
fac | either the name of a variable (column) in |
return_factor | if |
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 |
A represampling object, see also partition_cv for details.
In this partitioning approach, all repetition
s are identical and
therefore pseudo-replications.
sperrorest, partition_cv, as.resampling.factor
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] #> 150parti <- 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 #>