Training eta parameter for the varmer function evaluating a vector of etas using Cross-validation. The best eta is the one yielding the highest KGE metric.
1 2 3 4 5 6 7 8 9 | fit.varmer(
stations.sf,
v,
etas = c(10, 100, 500, 1000, 5000),
idw_formula = Variable ~ 1,
factor_agg = 2,
drty.out = tempdir(),
apply_varmer = T
)
|
stations.sf |
data.frame with the observations metadata |
v |
grided image |
etas |
(optional) vector of eta values to evaluate in a CV exercise |
idw_formula |
formula for the idw interpolation |
factor_agg |
scalar which defines the aggregation factor to apply to the raster images in order to reduce computation requirements for solving varmer |
drty.out |
(optional) output folder for the CV metrics |
apply_varmer |
(optional) boolean which determines if a merging image is produced with the best eta |
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