crossvalidateODE {CNORode} | R Documentation |
k-fold crossvalidation for logic ODE model
crossvalidateODE( CNOlist, model, nfolds = 10, foldid = NULL, type = "datapoint", parallel = FALSE, ode_parameters = NULL, paramsSSm = NULL, method = "essm" )
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 |
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.