runHclust {CytoTree} | R Documentation

## runHclust

### Description

Hierarchical cluster analysis on a set of dissimilarities and methods for analyzing it.

### Usage

    runHclust(
      object,
      k = 25,
      hclust.method = "complete",
      dist.method = "euclidean",
      verbose = FALSE
    )

### Arguments

`object` | a CYT object
`k` | numeric. The number of clusters.
`hclust.method` | character or a function. The agglomeration method to be used. This should be one of "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid". Or you can specify an equation as input, for example `function(x) hclust(x,method = 'ward.D2')`.
`dist.method` | character or a function. The distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Or you can specify an equation as input, for example `function(x) as.dist((1-cor(t(x)))/2)`.
`verbose` | logical. Whether to print calculation progress.

### Value

A CYT object with cluster

cyt.file <- system.file("extdata/cyt.rds", package = "CytoTree") cyt <- readRDS(file = cyt.file)

cyt <- runHclust(cyt, k = 9, verbose = TRUE)

### See Also

`hclust`, `dist`

[Package CytoTree version 1.0.3 Index]