CNORfuzzy (version 1.14.0)

plotMeanFuzzyFit: Simulates models returned from multiple cFL runs and plots mean fit to data

Description

Uses post refinement threshold (selection threshold) to choose reduced refined model resulting from each run. Simulates model and plots result and fit to data

Usage

plotMeanFuzzyFit(postRefThresh, allFinalMSEs, allRes, plotPDF=FALSE, tag=NULL,
show=TRUE, plotParams=list(cex=0.8, cmap_scale=1))

Arguments

postRefThresh

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

allFinalMSEs

matrix containing MSEs produced by compileMultiRes

allRes

list containing results of several CNORwrapFuzzy runs

plotPDF

TRUE or FALSE depending on if a PDF file should be saved

tag

String to include in filename of PDF plot

show

If the plot should be displayed

plotParams

a list of option related to the PDF and plotting outputs. (1) cex is the font size of the header. (2) cmap_scale below 1 allows to put more emphasizes on low errors (default 1 means all colors have the same weight). See plotOptimResultsPan from CellNOptR for other fields.

Value

This function does not have any output, it just plots and saves results if applicable.

Examples

    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)
#     plotMeanFuzzyFit(0.1, summary$allFinalMSEs, allRes)
# 
#     ## End(Not run)