adegenet (version 1.4-2)

dapcIllus: Simulated data illustrating the DAPC

Description

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

Usage

data(dapcIllus)

encoding

UTF-8

format

dapcIllus is list of 4 components being all genind objects.

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

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)