iClarans: A Clustering Algorithm based on Randomized Search with...
In QTCAT/iqtcat: Interaction Quantitative Trait Cluster Association Test
Description
Partitioning (clustering) into k clusters "around medoids" by
randomized search. 1-abs(cor) is used as distance.
Usage
1
iClarans(snp, iInx, k, maxNeigbours = 100, nLocal = 10, mc.cores = 1)
Arguments
snp
A object of class snpMatrix.
iInx
...
k
positive integer specifying the number of clusters, less than
the number of observations.
maxNeigbours
positive integer specifying the maximum number of
randomized searches.
nLocal
positive integer specifying the number of optimisation runs.
Columns have to be similar to snp.
mc.cores
number of cores for parallel computing. See mclapply
in package parallel for details.
QTCAT/iqtcat documentation built on May 8, 2019, 3:48 a.m.
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Description
Partitioning (clustering) into k clusters "around medoids" by randomized search. 1-abs(cor) is used as distance.
Usage
1 | iClarans(snp, iInx, k, maxNeigbours = 100, nLocal = 10, mc.cores = 1)
|
Arguments
snp |
A object of class snpMatrix. |
iInx |
... |
k |
positive integer specifying the number of clusters, less than the number of observations. |
maxNeigbours |
positive integer specifying the maximum number of randomized searches. |
nLocal |
positive integer specifying the number of optimisation runs.
Columns have to be similar to |
mc.cores |
number of cores for parallel computing. See |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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