mem_multithreshold {spatialRF} | R Documentation |
Moran's Eigenvector Maps for different distance thresholds
Description
Computes Moran's Eigenvector Maps of a distance matrix (using mem()
) over different distance thresholds.
Usage
mem_multithreshold(
distance.matrix = NULL,
distance.thresholds = NULL,
max.spatial.predictors = NULL
)
Arguments
distance.matrix |
Distance matrix. Default: |
distance.thresholds |
Numeric vector with distance thresholds defining neighborhood in the distance matrix, Default: |
max.spatial.predictors |
Maximum number of spatial predictors to generate. Only useful to save memory when the distance matrix |
Details
The function takes the distance matrix x
, computes its weights at difference distance thresholds, double-centers the resulting weight matrices with double_center_distance_matrix()
, applies eigen to each double-centered matrix, and returns eigenvectors with positive normalized eigenvalues for different distance thresholds.
Value
A data frame with as many rows as the distance matrix x
containing positive Moran's Eigenvector Maps. The data frame columns are named "spatial_predictor_DISTANCE_COLUMN", where DISTANCE is the given distance threshold, and COLUMN is the column index of the given spatial predictor.
Examples
if(interactive()){
#loading example data
data(distance_matrix)
#computing Moran's eigenvector maps for 0, 1000, and 2000 km
mem.df <- mem_multithreshold(
distance.matrix = distance_matrix,
distance.thresholds = c(0, 1000, 2000)
)
head(mem.df)
}