Description Usage Arguments Value
Performs an evaluation procedure where training and test sets can be allocated in different ways, while always respecting the ordering provided by time (models are trained in the past and tested in the relative future).
1 2 3 4 |
data |
full dataset |
nfolds |
number of folds for the data set to be separated into. |
FUN |
function with arguments
|
form |
a formula for model learning |
window |
type of blocked-time window ordering considered. Should be one of
|
fold.alloc.proc |
name of fold allocation function. Should be one of
|
alloc.pars |
parameters to pass onto |
removeSP |
argument that determines whether spatio-temporal blocks including the space being used for testing should be removed from the training set. Default is FALSE, meaning the information is not removed |
time |
column name of time-stamp in |
site_id |
column name of location identifier in |
.keepTrain |
if TRUE (default), instead of the results of
|
... |
other arguments to FUN |
If keepTrain
is TRUE
, a list where each slot
corresponds to one repetition or fold, containing a list with
slots results
containing the results of FUN
, and
train
containing a data.frame with the time
and
site_id
identifiers of the observations used in the training
step. Usually, the results of FUN
is a data.frame
with location identifier site_id
, time-stamp time
,
true values trues
and the workflow's predictions
preds
.
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