IntermediateProperties: Calculate the intermediate network properties in the discovery dataset

Description

These properties are need at every permutation: so they will be computed once.

Usage

IntermediateProperties(dData, dCorr, dNet, tNodeNames, moduleAssignments, modules)

Arguments

dData

scaled data matrix from the discovery dataset.

dCorr

matrix of correlation coefficients between all pairs of variables/nodes in the discovery dataset.

dNet

adjacency matrix of network edge weights between all pairs of nodes in the discovery dataset.

tNodeNames

a character vector of node names in the test dataset

moduleAssignments

a named character vector containing the module each node belongs to in the discovery dataset.

modules

a character vector of modules for which to calculate the module preservation statistics.

Value

a list containing three lists: a list of weighted degree vectors, a list of correlation coefficient vectors, and a list of node contribution vectors. There is one vector for each module in each list.

Details

Input expectations: Note that this function expects all inputs to be sensible, as checked by the R function 'checkUserInput' and processed by 'modulePreservation'. These requirements are:

The ordering of node names across 'dData', 'dCorr', and 'dNet' is consistent.
The columns of 'dData' are the nodes.
'dData' has been scaled by 'Scale'.
'dCorr' and 'dNet' are square matrices, and their rownames are identical to their column names.
'moduleAssigments' is a named character vector, where the names represent node labels found in the discovery dataset (e.g. 'dNet').