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Showing content from https://blasbenito.github.io/spatialRF/reference/plot_training_df_moran.html below:

Moran's I plots of a training data frame — plot_training_df_moran • spatialRF

Plots the the Moran's I test of the response and the predictors in a training data frame.

plot_training_df_moran(
  data = NULL,
  dependent.variable.name = NULL,
  predictor.variable.names = NULL,
  distance.matrix = NULL,
  distance.thresholds = NULL,
  fill.color = viridis::viridis(100, option = "F", direction = -1),
  point.color = "gray30"
)

data

Data frame with a response variable and a set of predictors. Default: NULL

dependent.variable.name

Character string with the name of the response variable. Must be in the column names of data. If the dependent variable is binary with values 1 and 0, the argument case.weights of ranger is populated by the function case_weights(). 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. Optionally, the result of auto_cor() or auto_vif() Default: NULL

distance.matrix

Squared matrix with the distances among the records in data. The number of rows of distance.matrix and data must be the same. If not provided, the computation of the Moran's I of the residuals is omitted. Default: NULL

distance.thresholds

Numeric vector, distances below each value are set to 0 on separated copies of the distance matrix for the computation of Moran's I at different neighborhood distances. If NULL, it defaults to seq(0, max(distance.matrix)/4, length.out = 2). Default: NULL

fill.color

Character vector with hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. viridis::viridis(100)). Default: viridis::viridis(100, option = "F", direction = -1)

point.color

Character vector with a color name (e.g. "red4"). Default: gray30

if(interactive()){

   #load example data
   data(plant_richness_df)
   data(distance_matrix)

   #plot Moran's I of training data
   plot_moran_training_data(
     data = plant_richness_df,
     dependent.variable.name = "richness_species_vascular",
     predictor.variable.names = colnames(plant_richness_df)[5:21],
     distance.matrix = distance_matrix,
     distance.thresholds = c(
       0,
       2000,
       4000,
       6000,
       8000
       )
     )
}

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