Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/clusterDistances.R
Calculate a matrix storing the dissimilarities between each pair of clusters (meta-clusters) across a pair of samples (templates) S1
and S2
. (i,j)th
entry of the matrix stores dissimilarity between i-th
cluster (meta-cluster) from S1
and the j-th
cluster (meta-cluster) from S2
.
1 | dist.matrix(object1,object2, dist.type = 'Mahalanobis')
|
object1 |
an object of class |
object2 |
an object of class |
dist.type |
character, indicating the method with which the dissimilarity between a pair of clusters (meta-clusters) is computed. Supported dissimilarity measures are: 'Mahalanobis', 'KL' and 'Euclidean', with the default is set to 'Mahalanobis' distance. |
Consider two FC samples/templates S1
and S2
with k1
and k2
clusters/meta-clusters. The dissimilarity between each pair of cluster (meta-clusters) across S1
and S2
is computed and stored in a (k1 x k2
) matrix. The dissimilarity between i-th
cluster (meta-cluster) from S1
and j-th
cluster (meta-cluster) from S2
is computed using function dist.cluster
.
dist.matrix
function returns a (k1 x k2
) matrix where k1
and k2
are the number of clusters (meta-clusters) in the first and the second samples (templates) respectively. (i,j)
th entry of the matrix contains the dissimilarity between the i-th
cluster (meta-cluster) from sample1 (template1) and the j-th
cluster (meta-cluster) from sample2 (template2).
Ariful Azad
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ## ------------------------------------------------
## load data and retrieve two samples
## ------------------------------------------------
library(healthyFlowData)
data(hd)
sample1 = exprs(hd.flowSet[[1]])
sample2 = exprs(hd.flowSet[[2]])
## ------------------------------------------------
## cluster sample using kmeans algorithm
## ------------------------------------------------
clust1 = kmeans(sample1, centers=4, nstart=20)
clust2 = kmeans(sample2, centers=4, nstart=20)
cluster.labels1 = clust1$cluster
cluster.labels2 = clust2$cluster
## ------------------------------------------------
## Create ClusteredSample object
## and compute the Mahalanobis distance between
## each pair of clsuters and save it in a matrix
## ------------------------------------------------
clustSample1 = ClusteredSample(labels=cluster.labels1, sample=sample1)
clustSample2 = ClusteredSample(labels=cluster.labels2, sample=sample2)
## compute the dissimilarity matrix
DM = dist.matrix(clustSample1, clustSample2, dist.type='Mahalanobis')
print(DM)
|
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