testSpatialCorrelation: Spatial correlation of presence records over predictive...

In BlasBenito/SDMworkshop: Streamlining the Development of Species Distribution Models

Description

Takes as input a set of coordinates and a brick or stack of environmental variables and applies Moran.I (Gittleman and Kot 1990) to analyze the spatial correlation of each variable for the given points

Usage

testSpatialCorrelation(xy, variables)

Arguments

xy	A data frame with coordinates x and y. Column names are irrelevant as long as the first column represents the x coordinate, and the second column represents the y coordinate.
variables	A raster brick or stack with environmental variables. Must be in the same reference system of xy.

Value

A data frame with the following columns:

• variable: variable name.

• observed: observed autocorrelation, Moran I. Values higher than the random expectation are considered significant.

• expected: random expectation of Moran I for the given data.

• p.value: p-value of the Moran I analysis.

Author(s)

Blas Benito <[e-mail]>. The function Moran.I is authored by Julien Dutheil <[e-mail]> and Emmanuel Paradis.

References

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

Examples

data("virtualSpecies")
data(europe2000)
sp.cor <- testSpatialCorrelation(
  xy = virtualSpecies$observed.presence,
  variables = europe2000
)
sp.cor

BlasBenito/SDMworkshop documentation built on March 4, 2020, 4:16 a.m.