adegenet (version 2.0.1)

dapcIllus: 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.

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.

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.

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)