## Results of the clustering algorithm performed over the K populations following admixture models.

### Description

Print the detected clusters among the populations under study. This method also prints the number of clusters, the p-values of statistical tests performed when building the clusters, the estimated weights of the unknown component distributions inside each cluster, and the discrepancy matrix. The latter represents some kind of distance between the populations.

### Usage

## S3 method for class 'admix_cluster'
print(x, ...)


### Arguments

 x An object of class 'admix_cluster' (see ?admix_clustering). ... further arguments passed to or from other methods.

### Author(s)

Xavier Milhaud xavier.milhaud.research@gmail.com

### Examples


## Simulate data (chosen parameters indicate 2 clusters (populations (1,3), (2,4))!):
list.comp <- list(f1 = "gamma", g1 = "exp",
f2 = "gamma", g2 = "exp",
f3 = "gamma", g3 = "gamma",
f4 = "gamma", g4 = "exp")
list.param <- list(f1 = list(shape = 16, rate = 4), g1 = list(rate = 1/3.5),
f2 = list(shape = 14, rate = 2), g2 = list(rate = 1/5),
f3 = list(shape = 16, rate = 4), g3 = list(shape = 12, rate = 2),
f4 = list(shape = 14, rate = 2), g4 = list(rate = 1/7))
A.sim <- rsimmix(n=2600, unknownComp_weight=0.8, comp.dist = list(list.comp$f1,list.comp$g1),
comp.param = list(list.param$f1, list.param$g1))$mixt.data B.sim <- rsimmix(n=3000, unknownComp_weight=0.7, comp.dist = list(list.comp$f2,list.comp$g2), comp.param = list(list.param$f2, list.param$g2))$mixt.data
C.sim <- rsimmix(n=3500, unknownComp_weight=0.6, comp.dist = list(list.comp$f3,list.comp$g3),
comp.param = list(list.param$f3, list.param$g3))$mixt.data D.sim <- rsimmix(n=4800, unknownComp_weight=0.5, comp.dist = list(list.comp$f4,list.comp$g4), comp.param = list(list.param$f4, list.param$g4))$mixt.data
## Look for the clusters:
list.comp <- list(f1 = NULL, g1 = "exp",
f2 = NULL, g2 = "exp",
f3 = NULL, g3 = "gamma",
f4 = NULL, g4 = "exp")
list.param <- list(f1 = NULL, g1 = list(rate = 1/3.5),
f2 = NULL, g2 = list(rate = 1/5),
f3 = NULL, g3 = list(shape = 12, rate = 2),
f4 = NULL, g4 = list(rate = 1/7))
clusters <- admix_clustering(samples = list(A.sim,B.sim,C.sim,D.sim), n_sim_tab = 8,
comp.dist=list.comp, comp.param=list.param, conf.level = 0.95,
parallel=FALSE, n_cpu=2)
print(clusters)