mem_multithreshold: Compute Moran's Eigenvector Maps across multiple distance...

In spatialRF: Easy Spatial Modeling with Random Forest

Compute Moran's Eigenvector Maps across multiple distance thresholds

Description

Computes Moran's Eigenvector Maps (MEMs) using mem() at multiple distance thresholds and combines them into a single data frame. This creates spatial predictors capturing patterns at different spatial scales.

Usage

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

Arguments

distance.matrix	Numeric distance matrix between spatial locations.
distance.thresholds	Numeric vector of distance thresholds. Each threshold defines the maximum distance for spatial neighbors at that scale. Default: NULL (automatically computed with default_distance_thresholds()).
max.spatial.predictors	Integer specifying the maximum number of spatial predictors to return. If the total number of MEMs exceeds this value, only the first max.spatial.predictors columns are returned. Default: NULL (no limit).

Details

This function generates spatial predictors at multiple spatial scales by computing MEMs at different distance thresholds. Different thresholds capture spatial patterns at different scales:

• Smaller thresholds (e.g., 0) capture fine-scale spatial patterns

• Larger thresholds capture broad-scale spatial patterns

Algorithm:

1. For each distance threshold, calls mem() to compute MEMs

2. Each mem() call applies the threshold, double-centers the matrix, and extracts positive eigenvectors

3. Combines all MEMs into a single data frame

4. Optionally limits the total number of predictors with max.spatial.predictors

The resulting MEMs are used as spatial predictors in rf_spatial() to model spatial autocorrelation at multiple scales simultaneously.

Value

Data frame with one row per observation (matching distance.matrix dimensions) and columns representing MEMs at different distance thresholds. Column names follow the pattern ⁠spatial_predictor_<threshold>_<number>⁠ (e.g., "spatial_predictor_0_1", "spatial_predictor_1000_2").

See Also

mem(), rf_spatial(), default_distance_thresholds(), double_center_distance_matrix()

Other spatial_analysis: filter_spatial_predictors(), mem(), moran(), moran_multithreshold(), pca(), pca_multithreshold(), rank_spatial_predictors(), residuals_diagnostics(), residuals_test(), select_spatial_predictors_recursive(), select_spatial_predictors_sequential()

Examples

data(plants_distance)

# Compute MEMs for multiple distance thresholds
mems <- mem_multithreshold(
  distance.matrix = plants_distance,
  distance.thresholds = c(0, 1000, 5000)
)

# View structure
head(mems)
dim(mems)

# Check column names showing threshold and predictor number
colnames(mems)[1:6]

# Limit number of spatial predictors
mems_limited <- mem_multithreshold(
  distance.matrix = plants_distance,
  distance.thresholds = c(0, 1000, 5000),
  max.spatial.predictors = 20
)
dim(mems_limited)

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.