writeFuzzyNetwork: Despict the network results of training a cFL model to data in multiple runs.

Description

Fuzzy network results output.

Usage

writeFuzzyNetwork(postRefThresh, allFinalMSEs, allRes, tag=NULL,verbose=FALSE)

Arguments

postRefThresh

Post refinement threshold (selection threshold) chosen from plot produced by compileMultiRes

allFinalMSEs

matrix containing MSEs produced by compileMultiRes

allRes

list containing all results produced by compileMultiRes

tag

String to include in filename of pdf plot

verbose

If extra warnings should be displayed

Value

This function does not have any output, it just writes a SIF file, an edge attribute file, and a node attribute file

Details

The weights of the edges are computed as the mean across models using post refinement threshold (selection threshold) to choose reduced refined model resulting from each run.

As with writeNetwork, this function maps back the edges weights from the optimised (expanded and compressed) model to the original model. The mapping back only works if the path has length 2 at most (i.e. you have node1-comp1-comp2-node2, where comp refer to nodes that have been compressed).

Examples

Run this code


        data(ToyModel, package="CellNOptR")
        data(CNOlistToy,package="CellNOptR")
        paramsList = defaultParametersFuzzy(CNOlistToy, ToyModel)
        N = 10
        allRes = list()
 ## Not run: 
#         for (i in 1:N){
#             Res = CNORwrapFuzzy(CNOlistToy, ToyModel, paramsList)
#             allRes[[i]] = Res
#         }
# 
#         summary = compileMultiRes(allRes)
#         summary$allFinalMSEs
#         summary$allFinalNumParams
#     
#         writeFuzzyNetwork(postRefThresh, summary$allFinalMSEs, allRes)
#     ## End(Not run)