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').