partition_cv_strat creates a set of sample indices
corresponding to cross-validation test and training sets.
partition_cv_strat( data, coords = c("x", "y"), nfold = 10, return_factor = FALSE, repetition = 1, seed1 = NULL, strat )
vector of length 2 defining the variables in
number of partitions (folds) in
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
character: column in
A represampling object, see also
partition_strat_cv, however, stratified with respect to the variable
data[,strat]; i.e., cross-validation partitioning is done within each set
levels(data[, strat])), and the
folds of all levels are combined into one cross-validation fold.
data(ecuador) parti <- partition_cv_strat(ecuador, strat = "slides", nfold = 5, repetition = 1 ) idx <- parti[["1"]][]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE")#>  0.9996672# always == 1 # Non-stratified cross-validation: parti <- partition_cv(ecuador, nfold = 5, repetition = 1) idx <- parti[["1"]][]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE")#>  1.009664# close to 1 because of large sample size, but with some random variation