identifyMisfitIndices: Identifying indices of poorly fitted measurements
In saezlab/CellNOpt-Feeder: Integration of CellNOptR to add missing links

Description Usage Arguments Details Value Author(s) Examples

This function identifies poorly fitted measurements for specific experimental conditions. It returns a list of possible indices and mse's pointing to possible connections to be added during the feeding process.

1	identifyMisfitIndices(cnolist = cnolist, model = model, simData = simData, mseThresh = 0.05)

`cnolist`	a cnolist structure, as produced by makeCNOlist
`model`	a model as returned by readSIF. Alternatively, the filename can also be provided.
`simData`	a matrix of simulated data values for a specific model as returned by plotLBodeFitness)
`mseThresh`	thrreshold parameter for minimal misfit to be considered (mseThresh = 0.05 by default)

This function computes the misfits (MSE values) between the actual measured data points and the data values for a specific set of inferred model parameters. Once the MSE values are calculated for each of the measurements over each experimental condition, the poorly fitted measurements are then identify. A measurement is considered as poorly fitted if the corresponding inferred MSE value is higher than the specified MSE threshold value (mseThresh).

this function returns a list with fields:

`indices`	a list of indices pointing to the poorly fitted measurements and the corresponding ms value
`use`	a matrix of use values indicating the mismatch between model simulations and data for each measurement at each experimental condition

E.Gjerga

data(ToyModel_Gene, package="CNORfeeder")
data(CNOlistToy_Gene, package="CNORfeeder")
data(indices,package="CNORfeeder")
data(database, package="CNORfeeder")

indices = identifyMisfitIndices(cnolist = cnolist, model = model,
                                simData = simData, mseThresh = 0.05)