R/filter_spatial_predictors.R
filter_spatial_predictors.Rd
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.
filter_spatial_predictors(
data = NULL,
predictor.variable.names = NULL,
spatial.predictors.df = NULL,
cor.threshold = 0.5
)
Data frame with a response variable and a set of predictors. Default: NULL
Character vector with the names of the predictive variables. Every element of this vector must be in the column names of data
. Default: NULL
Data frame of spatial predictors.
Numeric between 0 and 1, maximum Pearson correlation between any pair of the selected variables. Default: 0.50
A data frame with non-redundant spatial predictors.
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
)
}