filter_spatial_predictors: Remove redundant spatial predictors

In spatialRF: Easy Spatial Modeling with Random Forest

Remove redundant spatial predictors

Description

Removes spatial predictors that are highly correlated with other spatial predictors or with non-spatial predictors. Particularly useful when using multiple distance thresholds that produce correlated spatial predictors.

Usage

filter_spatial_predictors(
  data = NULL,
  predictor.variable.names = NULL,
  spatial.predictors.df = NULL,
  cor.threshold = 0.5
)

Arguments

data	Data frame containing the predictor variables. Default: NULL.
predictor.variable.names	Character vector of non-spatial predictor names. Must match column names in data. Can also be a variable_selection object. Default: NULL.
spatial.predictors.df	Data frame of spatial predictors (e.g., from mem_multithreshold()). Default: NULL.
cor.threshold	Numeric between 0 and 1 (recommended: 0.5 to 0.75). Maximum allowed absolute Pearson correlation. Default: 0.50.

Details

Filtering is performed in two steps:

1. Remove spatial predictors correlated with each other (using auto_cor())

2. Remove spatial predictors correlated with non-spatial predictors

This two-step process ensures the retained spatial predictors are independent of both each other and the environmental predictors, improving model interpretability and reducing multicollinearity.

Value

Data frame containing only spatial predictors with correlations below cor.threshold (both among themselves and with non-spatial predictors).

See Also

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

Examples

data(
  plants_df,
  plants_predictors,
  plants_distance
)

# Generate spatial predictors using multiple distance thresholds
mem.df <- mem_multithreshold(
  distance.matrix = plants_distance,
  distance.thresholds = c(0, 1000)
)

# Filter spatial predictors to remove redundancy
# Removes spatial predictors correlated > 0.50 with each other
# or with environmental predictors
spatial.predictors.filtered <- filter_spatial_predictors(
  data = plants_df,
  predictor.variable.names = plants_predictors,
  spatial.predictors.df = mem.df,
  cor.threshold = 0.50
)

# Check dimensions
ncol(mem.df)  # original number
ncol(spatial.predictors.filtered)  # after filtering

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