moran_multithreshold: Moran's I test on a numeric vector for different...

In BlasBenito/spatialRF: Easy Spatial Modeling with Random Forest

Moran's I test on a numeric vector for different neighborhoods

Description

Applies moran() to different distance thresholds at the same time.

Usage

moran_multithreshold(
  x = NULL,
  distance.matrix = NULL,
  distance.thresholds = NULL,
  verbose = TRUE
)

Arguments

x	Numeric vector, generally model residuals, Default: NULL
distance.matrix	Distance matrix among cases in x. The number of rows of this matrix must be equal to the length of x. 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
verbose	Logical, if TRUE, plots Moran's I values for each distance threshold. Default: TRUE

Details

Using different distance thresholds helps to take into account the uncertainty about what "neighborhood" means in ecological systems (1000km in geological time means little, but 100m might be quite a long distance for a tree to disperse seeds over), and allows to explore spatial autocorrelation of model residuals for several minimum-distance criteria at once.

Value

A named list with the slots:

• df: Data frame with the results of moran per distance threshold.

• plot: A plot of Moran's I across distance thresholds.

• max.moran: Maximum value of Moran's I across thresholds.

• max.moran.distance.threshold: Distance threshold with the maximum Moran's I value.

See Also

moran()

Examples

if(interactive()){

 #loading example data
 data(distance_matrix)
 data(plant_richness)

 #computing Moran's I for the response variable at several reference distances
 out <- moran_multithreshold(
   x = plant_richness$richness_species_vascular,
   distance.matrix = distance_matrix,
   distance.thresholds = c(0, 100, 1000, 10000),
   plot = TRUE
   )
 out

}

BlasBenito/spatialRF documentation built on Sept. 1, 2022, 1:42 p.m.