HGC.PlotARIs {HGC} | R Documentation |
The function cut the dendrogram into specific clusters at different levels and compared the clusterings with given labels using Adjusted Rand Index (ARI)
HGC.PlotARIs(tree, k.min, k.max, labels, return.ARI)
tree |
the input clustering tree saved as |
k.min |
the minimum number to cut the tree. |
k.max |
the maximum number to cut the tree. |
labels |
a data frame or a matrix to store the label information. Different labels should be in different columns and the users should name the columns correspondingly. |
return.ARI |
a bool variable to choose whether output the ARI matrix. |
ARI is a widely used index to evaluate the consistence between two partitions
of the same samples. This function will first cut a given tree into specific
number of clusters using the function cutree
. Then it calculates the
ARIs between the clustering result and the given labels
with the help of R package mclust
. The function does such cutting and
calculation for different ks between k.min and k.max. Finally it visualize
these results using a line chart. ARIs with different labels are shown as
different lines with different colors in the figure.
A line chart will be drawn and a matrix of the ARIs will be returned.
data(Pollen) Pollen.PCs <- Pollen[["PCs"]] Pollen.Label.Tissue <- Pollen[["Tissue"]] Pollen.Label.CellLine <- Pollen[["CellLine"]] Pollen.SNN <- SNN.Construction(Pollen.PCs) Pollen.ClusteringTree <- HGC.dendrogram(G = Pollen.SNN) Pollen.labels <- data.frame(Tissue = Pollen.Label.Tissue, CellLine = Pollen.Label.CellLine) HGC.PlotARIs(tree = Pollen.ClusteringTree, k.min = 2, k.max = 15, labels = Pollen.labels)