getTuningParameterConfiguration

Returns the lambda, theta, and alpha values for the tuning parameters of a regularized SEM with mixed penalty.

Usage

getTuningParameterConfiguration(
  regularizedSEMMixedPenalty,
  tuningParameterConfiguration
)

Arguments

regularizedSEMMixedPenalty

object of type regularizedSEMMixedPenalty (see ?mixedPenalty)

tuningParameterConfiguration

integer indicating which tuningParameterConfiguration should be extracted (e.g., 1). See the entry in the row tuningParameterConfiguration of regularizedSEMMixedPenalty@fits and regularizedSEMMixedPenalty@parameters.

Value

data frame with penalty and tuning parameter settings

Examples

library(lessSEM)

# Identical to regsem, lessSEM builds on the lavaan
# package for model specification. The first step
# therefore is to implement the model in lavaan.

dataset <- simulateExampleData()

lavaanSyntax <- "
f =~ l1*y1 + l2*y2 + l3*y3 + l4*y4 + l5*y5 + 
     l6*y6 + l7*y7 + l8*y8 + l9*y9 + l10*y10 + 
     l11*y11 + l12*y12 + l13*y13 + l14*y14 + l15*y15
f ~~ 1*f
"

lavaanModel <- lavaan::sem(lavaanSyntax,
                           data = dataset,
                           meanstructure = TRUE,
                           std.lv = TRUE)

# We can add mixed penalties as follows:

regularized <- lavaanModel |>
  # create template for regularized model with mixed penalty:
  mixedPenalty() |>
  # add penalty on loadings l6 - l10:
  addLsp(regularized = paste0("l", 11:15), 
         lambdas = seq(0,1,.1),
         thetas = 2.3) |>
  # fit the model:
  fit()

getTuningParameterConfiguration(regularizedSEMMixedPenalty = regularized, 
                                tuningParameterConfiguration = 2)