optimalKMinimizeAmbiguity {PDATK} | R Documentation

## Predict optimal K values by minimizing the difference between the ECDF of clustering consensus at two points in a subinterval.

### Description

Predict optimal K values by minimizing the difference between the ECDF of clustering consensus at two points in a subinterval.

### Usage

    optimalKMinimizeAmbiguity(assayClusters, subinterval = c(0.1, 0.9))

### Arguments

`assayClusters` | A `SimpleList` of clustering results from a `ConsensusMetaclusteringModel`, as returned by `models(object)` where object is a trained `ConsensusMetaclusteringModel` object.
`subinterval` | A `numeric` vector of two float values, the first being the lower and second being the upper limit of the subinteral to compare cluster ambiguity over. Default is c(0.1, 0.9), i.e. comparing the 10th and 90th percentile of cluster consensus to calculate the ambiguity of a given clustering solution. This is the value used to selected the optimal K value from the potential solutions for each assay in the training data.

### Value

A `numeric` vector the same length as `assayClusters`, with an optimal K prediction for each assay in the `rawdata` slot of the trained `ConsensusMetaclusteringModel` object which `assayClusters` came from.

[Package PDATK version 1.2.0 Index]