moranNP.randtest: Function to compute positive and negative parts of Moran's... In adespatial: Multivariate Multiscale Spatial Analysis

Description

This function computes positive and negative parts of Moran's I statistic and provide a testing procedure using random permutations. The functions compute the Moran's eigenvector maps (MEM) and eigenvalues for the `listw` object. If `alter = "greater"`, the statistic 'I+' is computed as the sum of the products between positive eigenvalues and squared correlations between `x` and associated MEMs. If `alter = "less"`, the statistic 'I-' is computed as the sum of the products between negative eigenvalues and squared correlations between `x` and associated MEMs. If ```alter = "two-sided"```, both statistics are computed.

Usage

 ```1 2``` ```moranNP.randtest(x, listw, nrepet = 999, alter = c("greater", "less", "two-sided"), ...) ```

Arguments

 `x` a `vector` with numeric data `listw` an object of class `listw` created for example by `nb2listw` `nrepet` an integer indicating the number of permutations used in the randomization procedure `alter` a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two-sided" `...` other arguments (e.g., `p.adjust.method`) to be passed to the codeas.krandtest function.

Value

An object of class `randtest` (for unilateral test) or `krandtest` (for bilateral test)

Author(s)

Stéphane Dray [email protected]

References

Dray, S. (2011). A new perspective about Moran’s coefficient: spatial autocorrelation as a linear regression problem. Geographical Analysis, 43, 127–141.

`moran.randtest`
 ```1 2 3 4 5 6 7 8``` ```if(require("ade4", quietly = TRUE) & require("spdep", quiet = TRUE)){ data(mafragh) tests <- moranNP.randtest(mafragh\$env[,1], nb2listw(mafragh\$nb), alter = "two-sided", p.adjust.method = "holm") tests moran.randtest(mafragh\$env[,1], nb2listw(mafragh\$nb))\$obs sum(tests\$obs) } ```