crossval | R Documentation |
Applies a cross-validation of DS results, using the same strategy as in the DS exercise. Any step-wise screening is applied for each iteration independently of that used to identify the subset of skillful predictors in the original analysis. The model coeffiecients (beta) is saved for each iteration, and both correlation and root-mean-squared-error are returned as scores.
crossval(x, m = 5, verbose = FALSE, ...)
x |
The results from |
m |
window with - leave m-out for each iteration. There are also some pre-set options: 'cordex-esd-exp1', 'value-exp1', and 'loo' for experiments defined at CORDEX-ESD, COST-VALUE, and leave-one-out ('loo') cross-validation. |
verbose |
if TRUE print progress |
... |
additional arguments |
crossval.dsensemble
will make use of the evaluation
attribute
with cross-validation results and returns the correlation.
Cross-validation object.
data(Oslo)
t2m <- t2m.DNMI(lon=c(-20,40),lat=c(45,65))
eof <- EOF(t2m)
ds <- DS(Oslo,eof)
xv <- crossval(ds)
plot(xv)
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