Create cross-validation indices
Divide indices from 1 to n into subsets for k-fold cross
validation. These functions are potentially useful when creating the
cvfits and cvfun arguments for
init_refmodel. The returned value is different for
these two methods, see below for details.
cvfolds(n, K, seed = NULL)
cv_ids(n, K, out = c("foldwise", "indices"), seed = NULL)n |
Number of data points. |
K |
Number of folds. Must be at least 2 and not exceed |
seed |
Random seed so that the same division could be obtained again if needed. |
out |
Format of the output, either 'foldwise' (default) or 'indices'. See below for details. |
cvfolds returns a vector of length n such that each
element is an integer between 1 and k denoting which fold the
corresponding data point belongs to. The returned value of cv_ids
depends on the out-argument. If out='foldwise', the returned
value is a list with k elements, each having fields tr and
ts which give the training and test indices, respectively, for the
corresponding fold. If out='indices', the returned value is a list
with fields tr and ts each of which is a list with k
elements giving the training and test indices for each fold.
### compute sample means within each fold n <- 100 y <- rnorm(n) cv <- cv_ids(n, K=5) cvmeans <- lapply(cv, function(fold) mean(y[fold$tr]))
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