newTau {lessSEM} | R Documentation |
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)
[Package lessSEM version 1.5.5 Index]