Plots the results of spatial autocorrelation tests for a variety of functions within the package. The x axis represents the Moran's I estimate, the y axis contains the values of the distance thresholds, the dot sizes represent the p-values of the Moran's I estimate, and the red dashed line represents the theoretical null value of the Moran's I estimate.

plot_moran(
model,
point.color = viridis::viridis(
100,
option = "F",
direction = -1
),
line.color = "gray30",
option = 1,
ncol = 1,
verbose = TRUE
)

## Arguments

model A model fitted with rf(), rf_repeat(), or rf_spatial(), or a data frame generated by moran(). Default: NULL Colors of the plotted points. Can be a single color name (e.g. "red4"), a 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") Character string, color of the line produced by ggplot2::geom_smooth(). Default: "gray30" Integer, type of plot. If 1 (default) a line plot with Moran's I and p-values across distance thresholds is returned. If 2, scatterplots of residuals versus lagged residuals per distance threshold and their corresponding slopes are returned. In models fitted with rf_repeat(), the residuals and lags of the residuals are computed from the median residuals across repetitions. Option 2 is disabled if x is a data frame generated by moran(). Number of columns of the plot. Only relevant when option = 2. Argument ncol of wrap_plots. Logical, if TRUE, the resulting plot is printed, Default: TRUE

## Value

A ggplot.

moran(), moran_multithreshold()

## Examples

if(interactive()){

data(plant_richness_df)
data(distance.matrix)

#fitting a random forest model
rf.model <- rf(
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, 1000, 2000),
n.cores = 1,
verbose = FALSE
)

#Incremental/multiscale Moran's I
plot_moran(rf.model)

#Moran's scatterplot
plot_moran(rf.model, option = 2)

}