dot-cvRegularizeSmoothSEMInternal: .cvRegularizeSmoothSEMInternal
In lessSEM: Non-Smooth Regularization for Structural Equation Models

.cvRegularizeSmoothSEMInternal

R Documentation

.cvRegularizeSmoothSEMInternal

Description

Combination of smoothly regularized structural equation model and cross-validation

Usage

.cvRegularizeSmoothSEMInternal(
  lavaanModel,
  k,
  standardize,
  penalty,
  weights,
  returnSubsetParameters,
  tuningParameters,
  epsilon,
  modifyModel,
  method = "bfgs",
  control
)

Arguments

`lavaanModel`	model of class lavaan
`k`	the number of cross-validation folds. Alternatively, a matrix with pre-defined subsets can be passed to the function. See ?lessSEM::cvSmoothLasso for an example
`standardize`	should training and test sets be standardized?
`penalty`	string: name of the penalty used in the model
`weights`	labeled vector with weights for each of the parameters in the model.
`returnSubsetParameters`	if set to TRUE, the parameter estimates of the individual cross-validation training sets will be returned
`tuningParameters`	data.frame with tuning parameter values
`epsilon`	epsilon > 0; controls the smoothness of the approximation. Larger values = smoother
`modifyModel`	used to modify the lavaanModel. See ?modifyModel.
`method`	optimizer used. Currently only "bfgs" is supported.
`control`	used to control the optimizer. This element is generated with the controlBFGS function. See ?controlBFGS for more details.

Details

Internal function: This function computes the regularized models for all penalty functions which are implemented for bfgs. Use the dedicated penalty functions (e.g., lessSEM::cvSmoothLasso) to penalize the model.