HGC.PlotARIs {HGC} | R Documentation

## Calculating and Visualizing ARIs of the clustering results with given labels

### Description

The function cut the dendrogram into specific clusters at different levels and compared the clusterings with given labels using Adjusted Rand Index (ARI)

### Usage

    HGC.PlotARIs(tree, k.min, k.max, labels, return.ARI)

### Arguments

`tree` | the input clustering tree saved as `hclust` data structure.
`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.

### Details

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.

### Value

A line chart will be drawn and a matrix of the ARIs will be returned.

### Examples

    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)

[Package HGC version 1.2.0 Index]