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
)
```

- model
A model fitted with

`rf()`

,`rf_repeat()`

, or`rf_spatial()`

, or a data frame generated by`moran()`

. Default:`NULL`

- point.color
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")`

- line.color
Character string, color of the line produced by

`ggplot2::geom_smooth()`

. Default:`"gray30"`

- option
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()`

.- ncol
Number of columns of the plot. Only relevant when

`option = 2`

. Argument`ncol`

of wrap_plots.- verbose
Logical, if

`TRUE`

, the resulting plot is printed, Default:`TRUE`

A ggplot.

```
if(interactive()){
#loading example data
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)
}
```