crossvalidateODE {CNORode}R Documentation

Crossvalidate ODE model

Description

k-fold crossvalidation for logic ODE model

Usage

crossvalidateODE(
  CNOlist,
  model,
  nfolds = 10,
  foldid = NULL,
  type = "datapoint",
  parallel = FALSE,
  ode_parameters = NULL,
  paramsSSm = NULL,
  method = "essm"
)

Arguments

CNOlist

Cnolist which contains all the experiments

model

a model prepared for the training

nfolds

number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets.

foldid

an optional vector of values between '1' and 'nfold' identifying what fold each observation is in. If supplied, 'nfold' can be missing.

type

define the way to do the crossvalidation. The default is 'type="datapoint"', which assigns the data randomly into folds. The option 'type="experiment"' uses whole experiments for crossvalidation (all data corresponding to a cue combination). The 'type=observable' uses the subset of nodes across all experiments for crossvalidation.

parallel

use for parallel execution, requires the doParallel package

ode_parameters

list of fitted logic ODE parameter

paramsSSm

parameters for the SSm optimizer for running the optimization in crossvalidation

method

Selection of optimization method: only "ga" or "essm" arguments are accepted

Details

Does a k-fold cross-validation for logic ODE CellNOpt models. In k-iterations a fraction of the data is eliminated from the CNOlist. The model is trained on the remaining data and then the model predicts the held-out data. Then the prediction accuracy is reported for each iteration.

See Also

parEstimationLBode


[Package CNORode version 1.35.1 Index]