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Plots a Moran's I test of model residuals

Source: R/plot_moran.R
plot_moran.Rd

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.

Usage

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

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

Value

A ggplot.

See also

moran(), moran_multithreshold()

Examples

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)

}