lessSEM2Lavaan

Creates a lavaan model object from lessSEM (only if possible). Pass either a criterion or a combination of lambda, alpha, and theta.

Usage

lessSEM2Lavaan(
  regularizedSEM,
  criterion = NULL,
  lambda = NULL,
  alpha = NULL,
  theta = NULL
)

Arguments

regularizedSEM

object created with lessSEM

criterion

criterion used for model selection. Currently supported are "AIC" or "BIC"

lambda

value for tuning parameter lambda

alpha

value for tuning parameter alpha

theta

value for tuning parameter theta

Value

lavaan model

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)

# Regularization:
regularized <- lasso(lavaanModel,
                     regularized = paste0("l", 11:15), 
                     lambdas = seq(0,1,.1))

# using criterion
lessSEM2Lavaan(regularizedSEM = regularized, 
               criterion = "AIC")
               
# using tuning parameters (note: we only have to specify the tuning
# parameters that are actually used by the penalty function. In case
# of lasso, this is lambda):
lessSEM2Lavaan(regularizedSEM = regularized, 
               lambda = 1)