View source: R/sperrorest_resampling.R
partition_cv_strat | R Documentation |
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 )
data |
|
coords |
vector of length 2 defining the variables in |
nfold |
number of partitions (folds) 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 |
seed1 |
|
strat |
character: column in |
A represampling object, see also partition_cv()
.
partition_strat_cv
, however, stratified with respect to the variable
data[,strat]
; i.e., cross-validation partitioning is done within each set
data[data[,strat]==i,]
(i
in levels(data[, strat])
), and the i
th
folds of all levels are combined into one cross-validation fold.
sperrorest()
, as.resampling()
, resample_strat_uniform()
data(ecuador) parti <- partition_cv_strat(ecuador, strat = "slides", nfold = 5, repetition = 1 ) idx <- parti[["1"]][[1]]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE") # always == 1 # Non-stratified cross-validation: parti <- partition_cv(ecuador, nfold = 5, repetition = 1) idx <- parti[["1"]][[1]]$train mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE") # close to 1 because of large sample size, but with some random variation
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