Complex Partial Least Squares Structural Equation Modeling • plssem

The goal of the plssem package is to allow the estimation of complex Structural Equation Models (SEMs) using the PLS-SEM framework. This package expands the PLS-SEM (and PLSc-SEM) framework to handle categorical data, non-linear models, and multilevel structures.

plssem is currently under development. The end goal is to allow the consistent estimation of non-linear multilevel PLS-SEM (and PLSc-SEM) models with ordinal/categorical data.

Installation

The package can be downloaded from CRAN.

install.packages("plssem")

The development version of the package can be installed from GitHub. It should be paired with the latest development version of modsem.

devtools::install_github(c("kss2k/modsem", "kss2k/plssem"))

Examples

Linear Model with Continuous Data

library(plssem)
library(modsem)

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
'

fit <- pls(tpb, TPB, bootstrap = TRUE)
summary(fit)

Linear Model with Ordered Data

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
'

fit <- pls(tpb, TPB_Ordered, bootstrap = TRUE)
summary(fit)

Multilevel Random Slopes Model with Continuous Data

syntax <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3
  W =~ w1 + w2 + w3
  Y ~ X + Z + (1 + X + Z | cluster)
  W ~ X + Z + (1 + X + Z | cluster)
"

fit <- pls(syntax, data = randomSlopes, bootstrap = TRUE)
summary(fit)

Multilevel Random Slopes Model with Ordered Data

syntax <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3
  W =~ w1 + w2 + w3
  Y ~ X + Z + (1 + X + Z | cluster)
  W ~ X + Z + (1 + X + Z | cluster)
"

fit <- pls(syntax, data = randomSlopesOrdered, bootstrap = TRUE)
summary(fit)

Multilevel Random Intercepts Model with Continuous Data

syntax <- '
  f =~ y1 + y2 + y3
  f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'

fit <- pls(syntax, data = randomIntercepts, bootstrap = TRUE)
summary(fit)

Multilevel Random Intercepts Model with Ordered Data

syntax <- '
  f =~ y1 + y2 + y3
  f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'

fit <- pls(syntax, data = randomInterceptsOrdered, bootstrap = TRUE)
summary(fit)

Interaction Model with Continuous Data

m <- '
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

  Y ~ X + Z + X:Z
'

fit <- pls(m, modsem::oneInt, bootstrap = TRUE)
summary(fit)

Interaction Model with Ordered Data

m <- '
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

  Y ~ X + Z + X:Z
'

fit <- pls(m, oneIntOrdered, bootstrap = TRUE)
summary(fit)

Multilevel Random Slopes Interaction Model with Continuous Data

syntax <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3
  W =~ w1 + w2 + w3
  Y ~ X + Z + X:Z + (1 + X + Z + X:Z | cluster)
  W ~ X + Z + X:Z + (1 + X + Z + X:Z | cluster)
"

fit <- pls(m, randomSlopes, bootstrap = TRUE)
summary(fit)

`plssem`