RuHere (version 1.0.1)

moranfast: Fast Moran's I Autocorrelation Index

Description

This function computes Moran's I autocorrelation coefficient for a numeric vector x using a matrix of weights. The method follows Gittleman and Kot (1990). This function is an implementation of ape::Moran.I(), but rewritten in C++ to be substantially faster and more memory-efficient.

Usage

moranfast(
  x,
  weight,
  na_rm = TRUE,
  scaled = FALSE,
  alternative = c("two.sided")
)

Value

A list with the following components:

• observed – The observed Moran's I.

• expected – The expected value of Moran's I under the null hypothesis.

• sd – The standard deviation of Moran's I under the null hypothesis.

• p.value – The p-value of the test based on the chosen alternative.

Arguments

x

(numeric) A numeric vector (e.g., environmental values extracted from occurrence records).

weight

(matrix) A matrix of spatial weights (e.g., a distance or inverse-distance matrix). The number of rows must be equal to the length of x.

na_rm

(logical) whether to remove missing values from x. Default is TRUE.

scaled

(logical) whether to scale Moran's I so that it ranges between –1 and +1. Default is TRUE.

alternative

(character) The alternative hypothesis tested against the null hypothesis of no autocorrelation. Must be one of "two.sided", "less", or "greater". Default is "two.sided".

References

Gittleman, J. L., & Kot, M. (1990). Adaptation: statistics and a null model for estimating phylogenetic effects. Systematic Zoology, 39(3), 227–241.

Examples

# Load example data
data("occurrences", package = "RuHere")
# Filter occurrences of Araucaria
occ <- occurrences[occurrences$species == "Araucaria angustifolia", ]
# Load example of raster variables
data("worldclim", package = "RuHere")
# Unwrap Packed raster
r <- terra::unwrap(worldclim)
# Extract values for bio_1
bio_1 <- terra::extract(r$bio_1,
                        occ[, c("decimalLongitude", "decimalLatitude")],
                        ID = FALSE, xy = TRUE)
#Remove NAs
bio_1 <- na.omit(bio_1)
# Convert values to numeric
v <- as.numeric(bio_1$bio_1)
# Compute geographic distance matrix
d <- fields::rdist.earth(x1 = as.matrix(bio_1[, c("x", "y")]), miles = FALSE)
# Inverse-distance weights
d <- 1/d
# Fill diagonal with 0
diag(d) <- 0
# Remove finite values
d[is.infinite(d)] <- 0
# Compute Moran's I
m <- moranfast(x = v, weight = d, scale = TRUE)
# Print results
m