modsem {modsem}

Interaction between latent variables

Description

modsem is a function for estimating interaction effects between latent variables, in structural equation models (SEM's). Methods for estimating interaction effects in SEM's can basically be split into two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", "ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). For the product indicator based approaces, modsem() is essentially a just 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 in seperately, and are 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", ...)

Arguments

model.syntax

lavaan syntax

data

dataframe

method

method to use: "rca" = residual centering approach (passed to lavaan), "uca" = unconstrained approach (passed to lavaan), "dblcent" = double centering approach (passed to lavaan), "pind" = prod ind approach, with no constraints or centering (passed to lavaan), "lms" = laten model structural equations (not passed to lavaan). "qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan). "custom" = use parameters specified in the function call (passed to lavaan)

...

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

Value

modsem object

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)

## Not run: 
# 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)


## End(Not run)

# 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)

## Not run: 
# 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")
summary(est_tpb_lms)

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

## End(Not run)