simpleHierarchicalConsensusCalculation: Simple hierarchical consensus calculation

Description

Hierarchical consensus calculation without calibration.

Usage

simpleHierarchicalConsensusCalculation(individualData, consensusTree, level = 1)

Value

A list with a single component consensus, containing the consensus data of the same dimensions as the individual entries in the input individualData. This perhaps somewhat cumbersome convention is used to make the output compatible with that of hierarchicalConsensusCalculation.

Arguments

individualData: Individual data from which the consensus is to be calculated. It can be either a list or a multiData structure. Each element in individulData should be a numeric object (vector, matrix or array).
consensusTree: A list specifying the consensus calculation. See details.
level: Integer which the user should leave at 1. This serves to keep default set names unique.

Author

Peter Langfelder

Details

This function calculates consensus in a hierarchical manner, using a separate (and possibly different) set of consensus options at each step. The "recipe" for the consensus calculation is supplied in the argument consensusTree.

The argument consensusTree should have the following components: (1) inputs must be either a character vector whose components match names(inputData), or consensus trees in the own right. (2) consensusOptions must be a list of class "ConsensusOptions" that specifies options for calculating the consensus. A suitable set of options can be obtained by calling newConsensusOptions. (3) Optionally, the component analysisName can be a single character string giving the name for the analysis. When intermediate results are returned, they are returned in a list whose names will be set from analysisName components, if they exist.

Unlike the similar function hierarchicalConsensusCalculation, this function ignores the calibration settings in the consensusOptions component of consensusTree; no calibration of input data is performed.