R: Monte Carlo test for sPCA

spca_randtest {adegenet} | R Documentation

## Monte Carlo test for sPCA

### Description

The function `spca_randtest` implements Monte-Carlo tests for the presence of significant spatial structures in a sPCA object. Two tests are run, for global (positive autocorrelation) and local (negative autocorrelation) structures, respectively. The test statistics used are the sum of the absolute values of the corresponding eigenvalues.

### Usage

    spca_randtest(x, nperm = 499)

### Arguments

`x` | A `spca` object.
`nperm` | The number of permutations to be used for the test.

### Value

A list with two objects of the class 'randtest' (see `as.randtest`), the first one for 'global' structures (positivie autocorrelation) and the second for 'local' structures (negative autocorrelation).

### Author(s)

Original code by Valeria Montano adapted by Thibaut Jombart.

### Examples

    ## Not run:
    ## Load data
    data(sim2pop)

    ## Make spca
    spca1 <- spca(sim2pop, type = 1, scannf = FALSE, plot.nb = FALSE)

    spca1
    plot(spca1)

    ## run tests (use more permutations in practice, e.g. 999)
    tests <- spca_randtest(spca1, nperm = 49)

    ## check results
    tests
    plot(tests[[1]]) # global structures

    ## End(Not run)

[Package adegenet version 2.1.4 Index]