listBuiltInFunctions {clusterExperiment} | R Documentation

## Built in ClusterFunction options

### Description

Documents the built-in clustering options that are available in the clusterExperiment package.

### Usage

    listBuiltInFunctions()

    ## S4 method for signature 'character'
    getBuiltInFunction(object)

    listBuiltInTypeK()

    listBuiltInType01()

### Arguments

`object` | name of built in function.

### Details

`listBuiltInFunctions` will return the character names of the built-in clustering functions available.

`listBuiltInTypeK` returns the names of the built-in functions that have type 'K'

`listBuiltInType01` returns the names of the built-in functions that have type '01'

`getBuiltInFunction` will return the `ClusterFunction` object of a character value that corresponds to a built-in function.

`algorithmType` and `inputType` will return the `algorithmType` and `inputType` of the built-in clusterFunction corresponding to the character value.

Built-in clustering methods: The built-in clustering methods, the names of which can be accessed by `listBuiltInFunctions()` are the following:

- "pam"Based on `pam` in `cluster` package. Arguments to that function can be passed via `clusterArgs`. Input can be either `"x"` or `"diss"`; algorithm type is "K"

- "clara"Based on `clara` in `cluster` package. Arguments to that function can be passed via `clusterArgs`. Note that we have changed the default arguments of that function to match the recommendations in the documentation of `clara` (numerous functions are set to less than optimal settings for back-compatiability). Specifically, the following defaults are implemented `samples=50`, `keep.data=FALSE`, `mediods.x=FALSE`,`rngR=TRUE`, `pamLike=TRUE`, `correct.d=TRUE`. Input is `"X"`; algorithm type is "K".

- "kmeans"Based on `kmeans` in `stats` package. Arguments to that function can be passed via `clusterArgs` except for `centers` which is reencoded here to be the argument 'k' Input is `"X"`; algorithm type is "K"

- "mbkmeans"`mbkmeans` runs mini-batch kmeans, a more computationally efficient version of kmeans.

- "hierarchical01"`hclust` in `stats` package is used to build hiearchical clustering. Arguments to that function can be passed via `clusterArgs`. The `hierarchical01` cuts the hiearchical tree based on the parameter `alpha`. It does not use the `cutree` function, but instead transversing down the tree until getting a block of samples with whose summary of the values is greater than or equal to 1-alpha. Arguments that can be passed to 'hierarchical01' are 'evalClusterMethod' which determines how to summarize the samples' values of D[samples,samples] for comparison to 1-alpha: "maximum" (default) takes the minimum of D[samples,samples] and requires it to be less than or equal to 1-alpha; "average" requires that each row mean of D[samples,samples] be less than or equal to 1-alpha. Additional arguments of hclust can also be passed via clusterArgs to control the hierarchical clustering of D. Input is `"diss"`; algorithm type is "01"

- "hierarchicalK"`hclust` in `stats` package is used to build hiearchical clustering and `cutree` is used to cut the tree into `k` clusters. Input is `"diss"`; algorithm type is "K"

- "tight"Based on the algorithm in Tsang and Wong, specifically their method of picking clusters from a co-occurance matrix after subsampling. The clustering encoded here is not the entire tight clustering algorithm, only that single piece that identifies clusters from the co-occurance matrix. Arguments for the tight method are 'minSize.core' (default=2), which sets the minimimum number of samples that form a core cluster. Input is `"diss"`; algorithm type is "01"

- "spectral"`specc` in `kernlab` package is used to perform spectral clustering. Note that spectral clustering can produce errors if the number of clusters (K) is not sufficiently smaller than the number of samples (N). K < N is not always sufficient. Input is `"X"`; algorithm type is "K".

### Value

`listBuiltInFunctions` returns a character vector of all the built-in cluster functions' names.

`getBuiltInFunction` returns the `ClusterFunction` object that corresponds to the character name of a function

`listBuiltInTypeK` returns a character vector of the names of built-in cluster functions that are of type "K"

`listBuiltInType01` returns a character vector of the names of built-in cluster functions that are of type "01"

### See Also

`ClusterFunction`, `algorithmType`, `inputType`

### Examples

    listBuiltInFunctions()
    algorithmType(c("kmeans","pam","hierarchical01"))
    inputType(c("kmeans","pam","hierarchical01"))
    listBuiltInTypeK()
    listBuiltInType01()

[Package clusterExperiment version 2.10.1 Index]