Combination of regularized structural equation model and cross-validation
.cvRegularizeSEMInternal(
lavaanModel,
k,
standardize,
penalty,
weights,
returnSubsetParameters,
tuningParameters,
method,
modifyModel,
control
)model of class cvRegularizedSEM
model of class lavaan
the number of cross-validation folds. Alternatively, a matrix with pre-defined subsets can be passed to the function. See ?lessSEM::cvLasso for an example
should training and test sets be standardized?
string: name of the penalty used in the model
labeled vector with weights for each of the parameters in the model.
if set to TRUE, the parameter estimates of the individual cross-validation training sets will be returned
data.frame with tuning parameter values
which optimizer should be used? Currently implemented are ista and glmnet. With ista, the control argument can be used to switch to related procedures (currently gist).
used to modify the lavaanModel. See ?modifyModel.
used to control the optimizer. This element is generated with the controlIsta() and controlGlmnet() functions.
Internal function: This function computes the regularized models for all penalty functions which are implemented for glmnet and gist. Use the dedicated penalty functions (e.g., lessSEM::cvLasso) to penalize the model.