View source: R/filter_spatial_predictors.R
filter_spatial_predictors | R Documentation |
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 |
Data frame with a response variable and a set of predictors. Default: |
predictor.variable.names |
Character vector with the names of the predictive variables. Every element of this vector must be in the column names of |
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: |
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 ) }
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