Compute correlogram based on the Moran's I index

Computes the Moran's I correlogram of a single or multiple variables.

Usage

lets.correl(x, y, z, equidistant = FALSE, plot = TRUE)

Arguments

x: A single numeric variable in vector format or multiple variables in matrix format (as columns).
y: A distance matrix of class matrix or dist.
z: The number of distance classes to use in the correlogram.
equidistant: Logical, if TRUE the classes will be equidistant. If FALSE the classes will have equal number of observations.
plot: Logical, if TRUE the correlogram will be ploted.

Value

Returns a matrix with the Moran's I Observed value, Confidence Interval (95 and Expected value. Also the p value of the randomization test, the mean distance between classes, and the number of observations. quase tudo

References

Sokal, R.R. & Oden, N.L. (1978) Spatial autocorrelation in biology. 1. Methodology. Biological Journal of the Linnean Society, 10, 199-228.

Sokal, R.R. & Oden, N.L. (1978) Spatial autocorrelation in biology. 2. Some biological implications and four applications of evolutionary and ecological interest. Biological Journal of the Linnean Society, 10, 229-249.

Author

Bruno Vilela, Fabricio Villalobos, Lucas Jardim & Jose Alexandre Diniz-Filho

Examples

if (FALSE) { # \dontrun{
data(PAM)
data(IUCN)

# Spatial autocorrelation in description year (species level)
midpoint <- lets.midpoint(PAM)
distan <- lets.distmat(midpoint[, 2:3])
moran <- lets.correl(IUCN$Description, distan, 12,
                     equidistant = FALSE, 
                     plot = TRUE)
                     
} # }