modsem (version 1.0.13)

modsem: Estimate interaction effects in structural equation models (SEMs)

Description

modsem() is a function for estimating interaction effects between latent variables in structural equation models (SEMs). Methods for estimating interaction effects in SEMs can basically be split into two frameworks:

1. Product Indicator (PI) based approaches ("dblcent", "rca", "uca", "ca", "pind")

2. Distributionally (DA) based approaches ("lms", "qml").

For the product indicator-based approaches, modsem() is essentially a fancy wrapper for lavaan::sem() which generates the necessary syntax and variables for the estimation of models with latent product indicators.

The distributionally based approaches are implemented separately and are not estimated using lavaan::sem(), but rather using custom functions (largely written in C++ for performance reasons). For greater control, it is advised that you use one of the sub-functions (modsem_pi, modsem_da, modsem_mplus) directly, as passing additional arguments to them via modsem() can lead to unexpected behavior.

Usage

modsem(model.syntax = NULL, data = NULL, method = "dblcent", ...)

Value

modsem object with class modsem_pi, modsem_da, or modsem_mplus

Arguments

model.syntax

lavaan syntax

data

dataframe

method

method to use:

"dblcent"

double centering approach (passed to lavaan).

"ca"

constrained approach (passed to lavaan).

"rca"

residual centering approach (passed to lavaan).

"uca"

unconstrained approach (passed to lavaan).

"pind"

prod ind approach, with no constraints or centering (passed to lavaan).

"lms"

latent moderated structural equations (not passed to lavaan).

"qml"

quasi maximum likelihood estimation (not passed to lavaan).

"custom"

use parameters specified in the function call (passed to lavaan).

"mplus"

estimate model through Mplus.

...

arguments passed to other functions depending on the method (see modsem_pi, modsem_da, and modsem_mplus)

Examples

library(modsem)
# For more examples, check README and/or GitHub.
# One interaction
m1 <- '
  # Outer Model
  X =~ x1 + x2 +x3
  Y =~ y1 + y2 + y3
  Z =~ z1 + z2 + z3

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

# Double centering approach
est1 <- modsem(m1, oneInt)
summary(est1)

if (FALSE) {
# The Constrained Approach
est1_ca <- modsem(m1, oneInt, method = "ca")
summary(est1_ca)

# LMS approach
est1_lms <- modsem(m1, oneInt, method = "lms")
summary(est1_lms)

# QML approach
est1_qml <- modsem(m1, oneInt, method = "qml")
summary(est1_qml)
}

# Theory Of Planned 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
'

# Double centering approach
est_tpb <- modsem(tpb, data = TPB)
summary(est_tpb)

if (FALSE) {
# The Constrained Approach
est_tpb_ca <- modsem(tpb, data = TPB, method = "ca")
summary(est_tpb_ca)

# LMS approach
est_tpb_lms <- modsem(tpb, data = TPB, method = "lms", nodes = 32)
summary(est_tpb_lms)

# QML approach
est_tpb_qml <- modsem(tpb, data = TPB, method = "qml")
summary(est_tpb_qml)
}