mop calculates a Mobility-Oriented Parity
mop( M_stack, G_stack, percent = 10, comp_each = 2000, parallel = FALSE, normalized = TRUE, ncores = 4 )
Arguments
| M_stack | a RasterStack of variables representing the calibration area (M area in ENM context). |
|---|---|
| G_stack | a RasterStack of variables representing areas or scenarios to which models will be transferred. |
| percent | (numeric) percent of values sampled from the calibration region to calculate the MOP. |
| comp_each | (numeric) compute distance matrix for a each fixed number of rows (default = 2000). |
| parallel | (logical) if TRUE, calculations will be performed in parallel using the available cores of the computer. This will demand more RAM and almost full use of the CPU; hence, its use is more recommended in high-performance computers. Using this option will speed up the analyses. Default = FALSE |
| normalized | (logical) if TRUE mop output will be normalized to 1. |
| ncores | (numeric) Number of cores to be used if the process is going to be run in parallel |
Value
A mobility-oriented parity RasterLayer where values of 0 represent strict extrapolation, which means complete dissimilarity of environments between the calibration (M) and projection area (G).
Details
The MOP is calculated following Owens et al. (2013; https://doi.org/10.1016/j.ecolmodel.2013.04.011). This function is a modification of the MOP function, available at https://github.com/narayanibarve/ENMGadgets.The value of the comp_each parameter depends on the RAM memory available for the process; the computation can be faster if the user chooses a bigger value for this parameter but you have to be careful on memory use.
Examples
m_stack <- raster::stack(list.files(system.file("extdata", package = "ntbox"), pattern = "M_layers.tif$", full.names = TRUE)) g_stack <- raster::stack(list.files(system.file("extdata", package = "ntbox"), pattern = "G_layers.tif$", full.names = TRUE)) mop_res <- mop(M_stack = m_stack, G_stack = g_stack, percent = 10, comp_each=2000)#> Computation progress: 50 %
