Cluster size statistics
SumOfRanges(x, cluster = 1)
SumOfVariances(x, cluster = 1)
SumOfVars(x, cluster = 1)
MeanCentroidDistance(x, cluster = 1)
MeanCentDist(x, cluster = 1)
MeanCentroidDist(x, cluster = 1)
DistanceFromMedian(x, cluster = 1)
DistFromMed(x, cluster = 1)
MeanNN(x, cluster = 1)
MeanMSTEdge(x, cluster = 1)
Matrix in which each row lists the coordinates of a point
in a Euclidian space; or, where supported, dist
object specifying
distances between each pair of points.
Optional integer vector specifying the cluster or group to
which each row in x
belongs.
SumOfRanges()
returns a numeric specifying the sum of ranges
within each cluster across all dimensions.
SumOfVariances()
returns a numeric specifying the sum of variances
within each cluster across all dimensions.
MeanCentroidDistance()
returns a numeric specifying the mean
distance from the centroid to points in each cluster.
DistanceFromMedian()
returns a numeric specifying the mean distance
of each point (except the median) from the median point of its cluster.
MeanNN()
returns a numeric specifying the mean distance from each
point within a cluster to its nearest neighbour.
MeanMSTEdge()
returns a numeric specifying the mean length of an
edge in the minimum spanning tree of points within each cluster.
Other tree space functions:
MSTSegments()
,
MapTrees()
,
MappingQuality()
,
SpectralEigens()
,
median.multiPhylo()
Other cluster functions:
KMeansPP()
points <- rbind(matrix(1:16, 4), rep(1, 4), matrix(1:32, 8, 4) / 10)
cluster <- rep(1:3, c(4, 1, 8))
plot(
points[, 1:2], # Plot first two dimensions of four-dimensional space
col = cluster, pch = cluster, # Style by cluster membership
asp = 1, # Fix aspect ratio to avoid distortion
ann = FALSE, frame = FALSE # Simple axes
)
SumOfRanges(points, cluster)
#> [1] 12.0 0.0 2.8
SumOfVariances(points, cluster)
#> [1] 6.666667 NA 0.240000
MeanCentroidDistance(points, cluster)
#> [1] 2.0 0.0 0.4
DistanceFromMedian(points, cluster)
#> [1] 2.6666667 NA 0.4571429
MeanNN(points, cluster)
#> [1] 2.0 NA 0.2
MeanMSTEdge(points, cluster)
#> [1] 2.0 NA 0.2