LMS and QML approaches

library(modsem)

The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) Approach

Both the LMS and QML approaches work on most models, but interaction effects with endogenous variables can be tricky to estimate (see the vignette). Both approaches, particularly the LMS approach, are computationally intensive and are partially implemented in C++ (using Rcpp and RcppArmadillo). Additionally, starting parameters are estimated using the double-centering approach, and the means of the observed variables are used to generate good starting parameters for faster convergence. If you want to monitor the progress of the estimation process, you can use verbose = TRUE.

A Simple Example

Here is an example of the LMS approach for a simple model. By default, the summary() function calculates fit measures compared to a null model (i.e., the same model without an interaction term).

library(modsem)
m1 <- '
# Outer Model
  X =~ x1
  X =~ x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

# Inner Model
  Y ~ X + Z
  Y ~ X:Z
'

lms1 <- modsem(m1, oneInt, method = "lms")
summary(lms1, standardized = TRUE) # Standardized estimates

Here is the same example using the QML approach:

qml1 <- modsem(m1, oneInt, method = "qml")
summary(qml1)

A More Complicated Example

Below is an example of a more complex model based on the theory of planned behavior (TPB), which includes two endogenous variables and an interaction between an endogenous and exogenous variable. When estimating more complex models with the LMS approach, it is recommended to increase the number of nodes used for numerical integration. By default, the number of nodes is set to 16, but this can be increased using the nodes argument. The nodes argument has no effect on the QML approach.

When there is an interaction effect between an endogenous and exogenous variable, it is recommended to use at least 32 nodes for the LMS approach. You can also obtain robust standard errors by setting robust.se = TRUE in the modsem() function.

Note: If you want the LMS approach to produce results as similar as possible to Mplus, you should increase the number of nodes (e.g., nodes = 100).

# ATT = Attitude
# PBC = Perceived Behavioral Control
# INT = Intention
# SN = Subjective Norms
# BEH = Behavior
tpb <- ' 
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC 
  BEH ~ INT:PBC  
'

lms2 <- modsem(tpb, TPB, method = "lms", nodes = 32)
summary(lms2)

qml2 <- modsem(tpb, TPB, method = "qml")
summary(qml2, standardized = TRUE) # Standardized estimates