Compute correlogram based on the Moran's I index

Share:

Description

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

Usage

1
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

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
## 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)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.