Clusters SNPs hierachically.
1  cluster.snp(x = NULL, d = NULL, method = "average", SNP_index = NULL)

x 
The SNP data matrix of size 
d 

method 
The agglomeration method to be used. This should be
(an unambiguous abbreviation of) one of 
SNP_index 

The SNPs are clustered using hclust
,
which performs a hierarchical cluster analysis using a set of dissimilarities
for the nvar
objects being clustered. There are 3 possible scenarios.
If d = NULL
, x
is used to compute the dissimilarity matrix.
The dissimilarity measure between two SNPs is 1  LD
(Linkage
Disequilibrium), where LD
is defined as the square of the Pearson
correlation coefficient. If SNP_index = NULL
, all nvar
SNPs will
be clustered; otherwise only the SNPs with indices specified by SNP_index
will be considered.
If the user wishes to use a different dissimilarity measure, d
needs
to be provided. d
must be either a square matrix of size
nvar x nvar
, or an object of class dist
. If d
is
provided, x
and SNP_index
will be ignored.
An object of class dendrogram
which describes the tree
produced by the clustering algorithm hclust
.
1 2 3 4 5 6 7  library(MASS)
x < mvrnorm(60,mu = rep(0,60), Sigma = diag(60))
clust.1 < cluster.snp(x = x, method = "average")
SNP_index < seq(1,10)
clust.2 < cluster.snp(x = x, method = "average", SNP_index = SNP_index)
d < dist(x)
clust.3 < cluster.snp(d = d, method = "single")

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