lets.correl {letsR} | R Documentation |
Compute correlogram based on the Moran's I index
Description
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 |
z |
The number of distance classes to use in the correlogram. |
equidistant |
Logical, if |
plot |
Logical, if |
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
Author(s)
Bruno Vilela, Fabricio Villalobos, Lucas Jardim & Jose Alexandre Diniz-Filho
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.
Examples
## Not run:
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)
## End(Not run)