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]