lessSEM (version 1.5.5)

getTuningParameterConfiguration: getTuningParameterConfiguration

Description

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

Usage

getTuningParameterConfiguration(
  regularizedSEMMixedPenalty,
  tuningParameterConfiguration
)

Value

data frame with penalty and tuning parameter settings

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.

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)