newTau: newTau

In lessSEM: Non-Smooth Regularization for Structural Equation Models

newTau

Description

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)

lessSEM documentation built on May 29, 2024, 7:10 a.m.