`R/mem_multithreshold.R`

`mem_multithreshold.Rd`

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 )

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 |

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.

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.

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) }