Remove redundant spatial predictors

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.

Value

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

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.

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
#> [1] 160
ncol(spatial.predictors.filtered)  # after filtering
#> [1] 154