adegenet (version 2.0.1)

global.rtest: Global and local tests

Description

These two Monte Carlo tests are used to assess the existence of global and local spatial structures. They can be used as an aid to interprete global and local components of spatial Principal Component Analysis (sPCA). They rely on the decomposition of a data matrix X into global and local components using multiple regression on Moran's Eigenvector Maps (MEMs). They require a data matrix (X) and a list of weights derived from a connection network. X is regressed onto global MEMs (U+) in the global test and on local ones (U-) in the local test. One mean $R^2$ is obtained for each MEM, the k highest being summed to form the test statistic. The reference distribution of these statistics are obtained by randomly permuting the rows of X.

Usage

global.rtest(X, listw, k = 1, nperm = 499)
local.rtest(X, listw, k = 1, nperm = 499)

Arguments

X

a data matrix, with variables in columns

listw

a list of weights of class listw. Can be obtained easily using the function chooseCN.

k

integer: the number of highest $R^2$ summed to form the test statistics

nperm

integer: the number of randomisations to be performed.

Value

An object of class randtest.

Details

This test is purely R code. A C or C++ version will be developped soon.

References

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity, 101, 92--103.

See Also

chooseCN, spca, monmonier

Examples

## Not run: 
#  data(sim2pop)
# if(require(spdep)){
# cn <- chooseCN(sim2pop@other$xy,ask=FALSE,type=1,plot=FALSE,res="listw")
# 
# # global test
# Gtest <- global.rtest(sim2pop@tab,cn)
# Gtest
# 
# # local test
# Ltest <- local.rtest(sim2pop@tab,cn)
# Ltest
# }
# ## End(Not run)