runCNORode {CNORode}R Documentation

runCNORode

Description

A one-line wrapper of the CNORode pipeline

Usage

runCNORode(
  model,
  data,
  compression = TRUE,
  results_folder = "CNORode_results",
  cutNONC = TRUE,
  expansion = FALSE,
  LB_n = 1,
  LB_k = 0.1,
  LB_tau = 0.01,
  UB_n = 5,
  UB_k = 0.9,
  UB_tau = 10,
  default_n = 3,
  default_k = 0.5,
  default_tau = 1,
  opt_n = TRUE,
  opt_k = TRUE,
  opt_tau = TRUE,
  random = TRUE,
  maxeval = 1e+05,
  maxtime = 60,
  transfer_function = 3,
  nan_fac = 1,
  lambda_tau = 0,
  lambda_k = 0
)

Arguments

model

A filename of prior knowledge network (PKN) in the SIF format

data

A measurement filename in the MIDAS format

compression

compress the prior knowledge network (TRUE), see preprocessing

results_folder

results folder for the analysis.

cutNONC

cut non-observable non-controllable node from PKN (TRUE), see preprocessing

expansion

expand OR gates in the PKN (FALSE), see preprocessing

LB_n

lower bound on parameter n, see createLBodeContPars

LB_k

lower bound on parameter k, see createLBodeContPars

LB_tau

lower bound on parameter tau, see createLBodeContPars

UB_n

upper bound on parameter n, see createLBodeContPars

UB_k

upper bound on parameter k, see createLBodeContPars

UB_tau

upper bound on parameter tau, see createLBodeContPars

default_n

default value of parameter n, see createLBodeContPars

default_k

default value of parameter k, see createLBodeContPars

default_tau

default value of parameter tau, see createLBodeContPars

opt_n

should parameter n be optimised, see createLBodeContPars

opt_k

should parameter k be optimised, see createLBodeContPars

opt_tau

should parameter tau be optimised, see createLBodeContPars

random

initial parameter vector generation (TRUE: random, FALSE: half of the LB-UB)

maxeval

maximum number of funciton evaluations in the optimisation, see parEstimationLBodeSSm

maxtime

maximum CPU time (in seconds) spent on optimisation before calling final refinement, see parEstimationLBodeSSm

transfer_function

trandfer function types represented by the edges, see parEstimationLBodeSSm

nan_fac

penalty for NA simulations, see parEstimationLBodeSSm

lambda_tau

regularisation penalty for tau parameters, see parEstimationLBodeSSm

lambda_k

regularisation penalty for k parameters for optimisation, see parEstimationLBodeSSm

Examples

## Not run: 
model = system.file("extdata", "ToyModelMMB_FeedbackAnd.sif",package="CNORode")
data = system.file("extdata", "ToyModelMMB_FeedbackAnd.csv", package="CNORode")
res = runCNORode(model,data,results_folder = "./results")

## End(Not run)

[Package CNORode version 1.36.0 Index]