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)