## ----include = FALSE----------------------------------------------------------
EVAL_DEFAULT <- FALSE
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = EVAL_DEFAULT
)

## ----setup--------------------------------------------------------------------
# library(modsem)

## -----------------------------------------------------------------------------
# m1 <- '
#   # Outer Model
#   X =~ x1 + x2 + x3
#   Y =~ y1 + y2 + y3
#   Z =~ z1 + z2 + z3
# 
#   # Inner Model
#   Y ~ X + Z + X:Z
# '
# 
# est1 <- modsem(m1, oneInt)
# summary(est1)

## -----------------------------------------------------------------------------
# est1 <- modsem(m1, oneInt, method = "lms")
# summary(est1)

## -----------------------------------------------------------------------------
# reg1 <- lm(y1 ~ x1*z1, oneInt)
# summary(reg1)

## -----------------------------------------------------------------------------
# est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "dblcent")
# summary(est2)

## -----------------------------------------------------------------------------
# m3 <- '
#   # Outer Model
#   X =~ x1 + x2 + x3
#   Y =~ y1 + y2 + y3
# 
#   # Inner Model
#   Y ~ X + z1 + X:z1
# '
# 
# est3 <- modsem(m3, oneInt, method = "dblcent", res.cov.method = "none")
# summary(est3)

## -----------------------------------------------------------------------------
# m4 <- '
# # Outer Model
# X =~ x1 + x2 + x3
# Y =~ y1 + y2 + y3
# Z =~ z1 + z2 + z3
# 
# # Inner Model
# Y ~ X + Z + Z:X + X:X
# '
# 
# est4 <- modsem(m4, oneInt, method = "qml")
# summary(est4)

## -----------------------------------------------------------------------------
# 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 + INT:PBC
# '
# 
# # The double-centering approach
# est_tpb <- modsem(tpb, TPB)
# 
# # Using the LMS approach
# est_tpb_lms <- modsem(tpb, TPB, method = "lms")
# summary(est_tpb_lms)

## -----------------------------------------------------------------------------
# m2 <- '
# ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5
# CAREER =~ career1 + career2 + career3 + career4
# SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6
# CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC
# '
# 
# est_jordan <- modsem(m2, data = jordan)
# est_jordan_qml <- modsem(m2, data = jordan, method = "qml")
# summary(est_jordan_qml)