getFitDT: The optimization function that finds the scaling factor for...
In CNORdt: Add-on to CellNOptR: Discretized time treatments

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/getFitDT.R

This function is called from gaBinaryDT. Using the model passed as input, it finds a scaling factor that minimizes the mean squared error between the data from the boolean simulation and the experimental data. A spline is fitted to the experimental data to allow this.

1	getFitDT(simResults, CNOlist, model, indexList, sizeFac = 1e-04, NAFac = 1, nInTot, boolUpdates, lowerB, upperB)

`simResults`	The simulation results as output from simulatorDT
`CNOlist`	A CNOlist on which the score is based (based on all valueSignals).
`model`	A model list.
`indexList`	A list of indexes of species stimulated/inhibited/signals, as produced by indexfinder applied on the model and CNOlist above.
`sizeFac`	The scaling factor for the size term in the objective function, default to 0.0001.
`NAFac`	The scaling factor for the NA term in the objective function, default to 1.
`nInTot`	The number of inputs in the model prior to cutting, used to normalise the size penalty.
`boolUpdates`	The number of synchronous updates performed by the boolean simulator.
`lowerB`	The lower bound for the optimized value of the scaling factor.
`upperB`	The upper bound for the optimized value of the scaling factor.

The function optim() is used to find the optimal scaling factor.

This function returns a list with elements:

`score`	The mean squared error between simulation and experiment with NA and model size penalties.
`estimate`	The scaling factor used to compare boolean and real data.
`xCoords`	The x-axis coordinates after multiplication with the scaling factor.
`yInter`	The interpolated values of the experimental data.
`yBool`	The boolean simulation results at each time point.

A. MacNamara

gaBinaryDT, simulatorDT

# this function is usually contained within gaBinaryDT
# but the output can be viewed as follows:

library(CellNOptR)
library(CNORdt)
data(CNOlistPB, package="CNORdt")
data(modelPB, package="CNORdt")

# pre-processing
indexOrig <- indexFinder(CNOlist=CNOlistPB, model=modelPB, verbose=TRUE)
fields4Sim <- prep4sim(model=modelPB)

boolUpdates = 10
simResults <- simulatorDT(
  CNOlist=CNOlistPB,
  model=modelPB,
  simList=fields4Sim,
  indices=indexOrig,
  boolUpdates=boolUpdates
)
simResults = convert2array(simResults, dim(CNOlistPB$valueSignals[[1]])[1],
length(modelPB$namesSpecies), boolUpdates)

optimRes <- getFitDT(
  simResults=simResults,
  CNOlist=CNOlistPB,
  model=modelPB,
  indexList=indexOrig,
  boolUpdates=boolUpdates,
  lowerB=0.8,
  upperB=10,
  nInTot=length(which(modelPB$interMat == -1))
)