Description Usage Arguments Value Author(s) References Examples
Derive buffer distances using the raster::distance
function, so that these can be used as predictors for spatial prediction i.e. to account for spatial proximity to low, medium and high values.
1 2 3 4 5 6 7 8 9 | ## S4 method for signature 'SpatialPointsDataFrame,SpatialPixelsDataFrame'
buffer.dist(
observations,
predictionDomain,
classes,
width,
parallel = TRUE,
...
)
|
observations |
SpatialPointsDataFrame. |
predictionDomain |
SpatialPixelsDataFrame. |
classes |
vector of selected points as factors. |
width |
maximum width for buffer distance. |
parallel |
optional parallelization setting. |
... |
optional arguments to pass to |
object of class SpatialPixelsDataFrame
with distances to points
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., and Gräler, B. (2018) Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. doi: 10.7717/peerj.5518
1 2 3 4 5 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.