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

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
# 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))
)
``` |

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