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transformCLPM

transformCLPM.Rd

Transform the parameters of a CLPM model. Returns a string with the transformations to be passed to lessSEM.

Usage

transformCLPM(CLPM, parameters, transformation)

Arguments

CLPM

model created with CLPM function

parameters

names of the parameters which should be transformed. Note: Should be of form p_(u)

transformation

which transformation should be used? Currently supported: "changepoint", or "measurementInvariance"

Value

list with (1) transformation: string to be passed to lessSEM as transformation and (2) regularized: vector specifying which parameters should be regularized.

Examples

# The following simulation and analysis of a random intercept cross-lagged panel model
# is based on the syntax from Jeroen D. Mulder & Ellen L. Hamaker (2021)
# Three Extensions of the Random Intercept Cross-Lagged Panel Model,
# Structural Equation Modeling: A Multidisciplinary Journal,
# 28:4, 638-648, DOI: 10.1080/10705511.2020.1784738
#
# See https://jeroendmulder.github.io/RI-CLPM/lavaan.html

library(lessTemplates)
library(lavaan)
library(lessSEM)

# Simulate Data
data <- simulateRICLPM()

# Set up model
model <- "
# autoregressive and cross-lagged parameters:
eta1_(u) ~ a11_(u)*eta1_(u-1) + a12_(u)*eta2_(u-1)
eta2_(u) ~ a21_(u)*eta1_(u-1) + a22_(u)*eta2_(u-1)

# covariances
eta1_(u) ~~ 0*eta2_(u) + v11*eta1_(u)
eta2_(u) ~~ v22*eta2_(u)

# Add observations:
eta1_(u) =~ 1*y1_(u)
eta2_(u) =~ 1*y2_(u)

y1_(u) ~~ 0*y1_(u)
y2_(u) ~~ 0*y2_(u)

# random intercepts
RI_eta1 =~ 1*y1_(u)
RI_eta2 =~ 1*y2_(u)

RI_eta1 ~~ vri11*RI_eta1 + vri12*RI_eta2
RI_eta2 ~~ vri22*RI_eta2
"

# create the lavaan syntax using lessTemplates:
m <- lessTemplates::CLPM(model = model,
                               data = data,
                               addManifestVar = "no")
# fit the model:
fit <- sem(model = m$model,
           data = m$data,
           meanstructure = TRUE,
           missing = "ml")
# get the parameter estimates
coef(fit)

# In the simulation, we assumed that the autoregressive and cross-lagged parameters
# all stay the same over time (e.g, a11_u1 = a11_u2 = a11_u3 = a11_u4 = a11_u5).
# In practice, however, we won't know that. In the following, we will test this
# automatically
transform <- lessTemplates::transformCLPM(
  CLPM = m,
  parameters = c("a11_(u)", "a12_(u)", "a21_(u)", "a22_(u)"),
  # check measurement invariance of these parameters:
  transformation = "measurementInvariance")

# fit using lasso regularization:
lf <- lasso(lavaanModel = fit,
            regularized = transform$regularized,
            nLambdas = 50,
            modifyModel = modifyModel(transformations = transform$transformation))
# let's plot the regularized parameters:
plot(lf)
# In the best model, all regularized parameters have been set to zero:
coef(lf, criterion = "BIC")
# We can conclude that the lasso regularization suggests invariance of the
# autoregressive and cross-lagged parameters