Description Usage Arguments Value References See Also Examples

Divide a categorical raster map into training and testing partitions.
A wrapper function for

`caret::createDataPartition`

(Kuhn, 2008) to divide a
categorical raster map into training and testing partitions.

1 |

`x` |
RasterLayer with categorical data |

`size` |
numeric value between zero and one indicating the proportion of non-NA cells that should be included in the training partition. Default is 0.5, which results in equally sized partitions |

`spatial` |
logical. If TRUE, the function returns a SpatialPoints object with the coordinates of cells in each partition. If FALSE, the cell numbers are returned |

`...` |
additional arguments (none) |

A list containing the following components:

`train`

a SpatialPoints object or numeric vector indicating the cells in the training partition

`test`

a SpatialPoints object or numeric vector indicating the cells in the testing partition

`all`

a SpatialPoints object or numeric vector indicating all non-NA cells in the study region

Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1-26.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Not run:
## Plum Island Ecosystems
## Load observed land use maps
obs <- ObsLulcRasterStack(x=pie,
pattern="lu",
categories=c(1,2,3),
labels=c("forest","built","other"),
t=c(0,6,14))
## create equally sized training and testing partitions
part <- partition(x=obs[[1]], size=0.1, spatial=FALSE)
names(part)
## End(Not run)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.