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"
)

## Arguments

data Data frame with a response variable and a set of predictors. Default: NULL 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 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 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 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 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) Character vector with a color name (e.g. "red4"). Default: gray30

## Value

A ggplot2 object.

## Examples

if(interactive()){

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
)
)
}