A decomposition of the Moran coefficient in order to separately test for the simultaneous presence of positive and negative autocorrelation in a variable.
a vector or matrix
spatial connectivity matrix
number of iterations to simulate the null distribution
x is a matrix, this function computes the Moran
test for spatial autocorrelation for each column.
The p-values calculated for
a directed alternative hypothesis. Statistical significance is assessed
using a permutation procedure to generate a simulated null distribution.
data.frame that contains the following information
for each variable:
observed value of Moran's I (positive part)
variance of Moran's I (positive part)
simulated p-value of Moran's I (positive part)
observed value of Moran's I (negative part)
variance of Moran's I (negative part)
simulated p-value of Moran's I (negative part)
simulated p-value of the two-sided test
Dary, Stéphane (2011): A New Perspective about Moran’s Coefficient: Spatial Autocorrelation as a Linear Regression Problem. Geographical Analysis, 43 (2): pp. 127 - 141.
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