`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:

`NULL`

.- distance.thresholds
Numeric vector with distance thresholds defining neighborhood in the distance matrix, Default:

`NULL`

.- max.spatial.predictors
Maximum number of spatial predictors to generate. Only useful to save memory when the distance matrix

`x`

is very large. Default:`NULL`

.

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)
}
```