filter_spatial_predictors: Removes redundant spatial predictors

In spatialRF: Easy Spatial Modeling with Random Forest

Removes redundant spatial predictors

Description

Removes spatial predictors that are pair-wise correlated with other spatial predictors (which happens when there are several close distance thresholds), and spatial predictors correlated with non-spatial predictors.

Usage

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

Arguments

data	Data frame with a response variable and a set of predictors. Default: NULL
predictor.variable.names	Character vector with the names of the predictive variables. Every element of this vector must be in the column names of data. Default: NULL
spatial.predictors.df	Data frame of spatial predictors.
cor.threshold	Numeric between 0 and 1, maximum Pearson correlation between any pair of the selected variables. Default: 0.50

Value

A data frame with non-redundant spatial predictors.

Examples

if(interactive()){

#loading data
data("distance_matrix")
data("plant_richness_df")

#computing Moran's Eigenvector Maps
spatial.predictors.df <- mem_multithreshold(
  distance_matrix = distance_matrix,
  distance.thresholds = c(0, 1000)
  )

#filtering spatial predictors
spatial.predictors.df <- filter_spatial_predictors(
  data = plant_richness_df,
  predictor.variable.names = colnames(plant_richness_df)[5:21],
  spatial.predictors.df = spatial.predictors.df,
  cor.threshold = 0.50
 )


}

spatialRF documentation built on Aug. 19, 2022, 5:23 p.m.