R: Simulated data illustrating the DAPC

dapcIllus {adegenet} | R Documentation

## Simulated data illustrating the DAPC

### Description

Datasets illustrating the Discriminant Analysis of Principal Components (DAPC, Jombart et al. submitted).

### Format

`dapcIllus` is list of 4 components being all genind objects.

### Details

These data were simulated using various models using Easypop (2.0.1). The `dapcIllus` is a list containing the following genind objects:
- "a": island model with 6 populations
- "b": hierarchical island model with 6 populations (3,2,1)
- "c": one-dimensional stepping stone with 2x6 populations, and a boundary between the two sets of 6 populations
- "d": one-dimensional stepping stone with 24 populations

See "source" for a reference providing simulation details.

### Author(s)

Thibaut Jombart t.jombart@imperial.ac.uk

### Source

Jombart, T., Devillard, S. and Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. Submitted to BMC genetics.

### References

Jombart, T., Devillard, S. and Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. Submitted to Genetics.

### See Also

- `dapc`: implements the DAPC.

- `eHGDP`: dataset illustrating the DAPC and `find.clusters`.

- `H3N2`: dataset illustrating the DAPC.

- `find.clusters`: to identify clusters without prior.

### Examples

    ## Not run:

    data(dapcIllus)
    attach(dapcIllus)
    a # this is a genind object, like b, c, and d.

    ## FINS CLUSTERS EX NIHILO
    clust.a <- find.clusters(a, n.pca=100, n.clust=6)
    clust.b <- find.clusters(b, n.pca=100, n.clust=6)
    clust.c <- find.clusters(c, n.pca=100, n.clust=12)
    clust.d <- find.clusters(d, n.pca=100, n.clust=24)

    ## examin outputs
    names(clust.a)
    lapply(clust.a, head)

    ## PERFORM DAPCs
    dapc.a <- dapc(a, pop=clust.a$grp, n.pca=100, n.da=5)
    dapc.b <- dapc(b, pop=clust.b$grp, n.pca=100, n.da=5)
    dapc.c <- dapc(c, pop=clust.c$grp, n.pca=100, n.da=11)
    dapc.d <- dapc(d, pop=clust.d$grp, n.pca=100, n.da=23)

    ## LOOK AT ONE RESULT
    dapc.a
    summary(dapc.a)

    ## FORM A LIST OF RESULTS FOR THE 4 DATASETS
    lres <- list(dapc.a, dapc.b, dapc.c, dapc.d)

    ## DRAW 4 SCATTERPLOTS
    par(mfrow=c(2,2))
    lapply(lres, scatter)

    # detach data
    detach(dapcIllus)

    ## End(Not run)

[Package adegenet version 2.1.4 Index]