newTau

assign new value to parameter tau used by approximate optimization. Any regularized value below tau will be evaluated as zeroed which directly impacts the AIC, BIC, etc.

Usage

newTau(regularizedSEM, tau)

Arguments

regularizedSEM

object fitted with approximate optimization

tau

new tau value

Value

regularizedSEM, but with new regularizedSEM@fits$nonZeroParameters

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:

lsem <- smoothLasso(
  # pass the fitted lavaan model
  lavaanModel = lavaanModel,
  # names of the regularized parameters:
  regularized = paste0("l", 6:15),
  epsilon = 1e-10,
  tau = 1e-4,
  lambdas = seq(0,1,length.out = 50))
newTau(regularizedSEM = lsem, tau = .1)