vignettes/cSEM.R

In M-E-Rademaker/cSEM: Composite-Based Structural Equation Modeling

## ----eval = FALSE-------------------------------------------------------------
# csem(.data = my_data, .model = my_model)

## -----------------------------------------------------------------------------
model <- "
# Structural model
EXPE ~ IMAG

# Reflective measurement model
EXPE =~ expe1 + expe2
IMAG =~ imag1 + imag2
"

## ----eval=FALSE---------------------------------------------------------------
# model <- "
# # Structural model
# EXPE ~ IMAG
# QUAL ~ EXPE
# VAL  ~ EXPE + QUAL
# SAT  ~ IMAG + EXPE + QUAL + VAL
# LOY  ~ IMAG + SAT
# 
# # Composite model
# IMAG <~ imag1 + imag2 + imag3                  # composite
# EXPE <~ expe1 + expe2 + expe3                  # composite
# QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5  # composite
# VAL  <~ val1  + val2  + val3                   # composite
# 
# # Reflective measurement model
# SAT  =~ sat1  + sat2  + sat3  + sat4           # common factor
# LOY  =~ loy1  + loy2  + loy3  + loy4           # common factor
# 
# # Measurement error correlation
# sat1 ~~ sat2
# "

## -----------------------------------------------------------------------------
model <- "
# Structural model
EXPE ~ IMAG + IMAG.IMAG

# Composite model
EXPE <~ expe1 + expe2
IMAG <~ imag1 + imag2
"

## ----eval=FALSE---------------------------------------------------------------
# model <- "
# # Structural model
# SAT ~ QUAL
# VAL ~ SAT + QUAL
# 
# # Reflective measurement model
# SAT  =~ sat1 + sat2
# VAL  =~ val1 + val2
# 
# # Composite model
# IMAG <~ imag1 + imag2
# EXPE <~ expe1 + expe2
# 
# # Second-order term
# QUAL =~ IMAG + EXPE
# "

## ----warning=FALSE, message=FALSE---------------------------------------------
require(cSEM)

model <- "
# Path model / Regressions
eta2 ~ eta1
eta3 ~ eta1 + eta2

# Reflective measurement model
eta1 =~ y11 + y12 + y13
eta2 =~ y21 + y22 + y23
eta3 =~ y31 + y32 + y33
"

a <- csem(.data = threecommonfactors, .model = model)
a

## ----eval=FALSE---------------------------------------------------------------
# csem(
#    .data                        = threecommonfactors,
#    .model                       = model,
#    .approach_cor_robust         = "none",
#    .approach_nl                 = "sequential",
#    .approach_paths              = "OLS",
#    .approach_weights            = "PLS-PM",
#    .conv_criterion              = "diff_absolute",
#    .disattenuate                = TRUE,
#    .dominant_indicators         = NULL,
#    .estimate_structural         = TRUE,
#    .id                          = NULL,
#    .iter_max                    = 100,
#    .normality                   = FALSE,
#    .PLS_approach_cf             = "dist_squared_euclid",
#    .PLS_ignore_structural_model = FALSE,
#    .PLS_modes                   = NULL,
#    .PLS_weight_scheme_inner     = "path",
#    .reliabilities               = NULL,
#    .starting_values             = NULL,
#    .tolerance                   = 1e-05,
#    .resample_method             = "none",
#    .resample_method2            = "none",
#    .R                           = 499,
#    .R2                          = 199,
#    .handle_inadmissibles        = "drop",
#    .user_funs                   = NULL,
#    .eval_plan                   = "sequential",
#    .seed                        = NULL,
#    .sign_change_option          = "no"
#     )

## ----echo=FALSE, include=FALSE------------------------------------------------
x <- runif(1) # to intialize .Random.seed

## -----------------------------------------------------------------------------
b1 <- csem(.data = threecommonfactors, .model = model, .resample_method = "bootstrap")
b2 <- resamplecSEMResults(a)

## -----------------------------------------------------------------------------
summarize(b1)

## -----------------------------------------------------------------------------
ii <- infer(b1, .quantity = c("CI_standard_z", "CI_percentile"), .alpha = c(0.01, 0.05))
ii$Path_estimates

## ----eval=FALSE---------------------------------------------------------------
# b <- csem(
#   .data            = satisfaction,
#   .model           = model,
#   .resample_method = "bootstrap",
#   .R               = 999,
#   .seed            = 98234,
#   .eval_plan       = "multiprocess")
# 
# # Output omitted

## ----eval=FALSE---------------------------------------------------------------
# model <- "
# ## Structural model
# eta2 ~ eta1
# 
# ## Measurement model
# eta1 <~ y11 + y12 + y13
# eta2 =~ y21 + y22 + y23
# "
# 
# # Identical
# csem(threecommonfactors, model)
# csem(threecommonfactors, model, .disattenuate = TRUE)
# 
# # To supress automatic disattenuation
# csem(threecommonfactors, model, .disattenuate = FALSE)

## ----eval=FALSE---------------------------------------------------------------
# model <- "
# ## Structural model
# eta2 ~ eta1
# 
# ## Composite model
# eta1 <~ y11 + y12 + y13
# eta2 <~ y21 + y22 + y23
# "
# 
# ### Currently the following weight approaches are implemented
# # Partial least squares path modeling (PLS)
# csem(threecommonfactors, model, .approach_weights = "PLS-PM") # default
# 
# # Generalized canonical correlation analysis (Kettenring approaches)
# csem(threecommonfactors, model, .approach_weights = "SUMCORR")
# csem(threecommonfactors, model, .approach_weights = "MAXVAR")
# csem(threecommonfactors, model, .approach_weights = "SSQCORR")
# csem(threecommonfactors, model, .approach_weights = "MINVAR")
# csem(threecommonfactors, model, .approach_weights = "GENVAR")
# 
# # Generalized structured component analysis (GSCA)
# csem(threecommonfactors, model, .approach_weights = "GSCA")
# 
# # Principal component analysis (PCA)
# csem(threecommonfactors, model, .approach_weights = "PCA")
# 
# # Factor score regression (FSR) using "unit", "bartlett" or "regression" weights
# csem(threecommonfactors, model, .approach_weights = "unit")
# csem(threecommonfactors, model, .approach_weights = "bartlett")
# csem(threecommonfactors, model, .approach_weights = "regression")