spcvFoldsPoly: Make spatial partitions (using k-means clustering)
In jngtz/runout.opt: GPP regional runout model optimization and validation in R
Description
Usage
Arguments
Value
View source: R/randomwalk_spatial_cross_validation.R
Description
Divides a spatial sample of polygons into irregularly shaped spatial partitions
using k-means clustring. It uses the spatial coordinates defined by the polygon centroids.
Usage
1
spcvFoldsPoly(x, n_folds = 5, seed_cv = 11082017)
Arguments
x
A SpatialPolygonsDataframe
n_folds
The number of folds (i.e. partitions)
seed_cv
The random seed defined to reproduce results
Value
A vector containing numeric labels defining each fold
jngtz/runout.opt documentation built on Sept. 8, 2021, 2:15 p.m.
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Description Usage Arguments Value
View source: R/randomwalk_spatial_cross_validation.R
Description
Divides a spatial sample of polygons into irregularly shaped spatial partitions using k-means clustring. It uses the spatial coordinates defined by the polygon centroids.
Usage
1 | spcvFoldsPoly(x, n_folds = 5, seed_cv = 11082017)
|
Arguments
x |
A SpatialPolygonsDataframe |
n_folds |
The number of folds (i.e. partitions) |
seed_cv |
The random seed defined to reproduce results |
Value
A vector containing numeric labels defining each fold
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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