Computes Moran's Eigenvector Maps of a distance matrix (using mem()) over different distance thresholds.

mem_multithreshold(
distance.matrix = NULL,
distance.thresholds = NULL,
max.spatial.predictors = NULL
)

## Arguments

distance.matrix Distance matrix. Default: NULL. Numeric vector with distance thresholds defining neighborhood in the distance matrix, Default: NULL. Maximum number of spatial predictors to generate. Only useful to save memory when the distance matrix x is very large. Default: NULL.

## 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.

## 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.

## Examples

if(interactive()){

}