# NOT RUN {
# ===========================================================================
# Basic usage
# ===========================================================================
### Linear model ------------------------------------------------------------
# Most basic usage requires a dataset and a model. We use the
# `threecommonfactors` dataset.
## Take a look at the dataset
#?threecommonfactors
## Specify the (correct) model
model <- "
# Structural model
eta2 ~ eta1
eta3 ~ eta1 + eta2
# (Reflective) measurement model
eta1 =~ y11 + y12 + y13
eta2 =~ y21 + y22 + y23
eta3 =~ y31 + y32 + y33
"
## Estimate
res <- csem(threecommonfactors, model)
## Postestimation
verify(res)
summarize(res)
assess(res)
# Notes:
# 1. By default no inferential quantities (e.g. Std. errors, p-values, or
# confidence intervals) are calculated. Use resampling to obtain
# inferential quantities. See "Resampling" in the "Extended usage"
# section below.
# 2. `summarize()` prints the full output by default. For a more condensed
# output use:
print(summarize(res), .full_output = FALSE)
## Dealing with endogeneity -------------------------------------------------
# See: ?testHausman()
### Models containing second constructs--------------------------------------
## Take a look at the dataset
#?dgp_2ndorder_cf_of_c
model <- "
# Path model / Regressions
c4 ~ eta1
eta2 ~ eta1 + c4
# Reflective measurement model
c1 <~ y11 + y12
c2 <~ y21 + y22 + y23 + y24
c3 <~ y31 + y32 + y33 + y34 + y35 + y36 + y37 + y38
eta1 =~ y41 + y42 + y43
eta2 =~ y51 + y52 + y53
# Composite model (second order)
c4 =~ c1 + c2 + c3
"
res_2stage <- csem(dgp_2ndorder_cf_of_c, model, .approach_2ndorder = "2stage")
res_mixed <- csem(dgp_2ndorder_cf_of_c, model, .approach_2ndorder = "mixed")
# The standard repeated indicators approach is done by 1.) respecifying the model
# and 2.) adding the repeated indicators to the data set
# 1.) Respecify the model
model_RI <- "
# Path model / Regressions
c4 ~ eta1
eta2 ~ eta1 + c4
c4 ~ c1 + c2 + c3
# Reflective measurement model
c1 <~ y11 + y12
c2 <~ y21 + y22 + y23 + y24
c3 <~ y31 + y32 + y33 + y34 + y35 + y36 + y37 + y38
eta1 =~ y41 + y42 + y43
eta2 =~ y51 + y52 + y53
# c4 is a common factor measured by composites
c4 =~ y11_temp + y12_temp + y21_temp + y22_temp + y23_temp + y24_temp +
y31_temp + y32_temp + y33_temp + y34_temp + y35_temp + y36_temp +
y37_temp + y38_temp
"
# 2.) Update data set
data_RI <- dgp_2ndorder_cf_of_c
coln <- c(colnames(data_RI), paste0(colnames(data_RI), "_temp"))
data_RI <- data_RI[, c(1:ncol(data_RI), 1:ncol(data_RI))]
colnames(data_RI) <- coln
# Estimate
res_RI <- csem(data_RI, model_RI)
summarize(res_RI)
### Multigroup analysis -----------------------------------------------------
# See: ?testMGD()
# ===========================================================================
# Extended usage
# ===========================================================================
# `csem()` provides defaults for all arguments except `.data` and `.model`.
# Below some common options/tasks that users are likely to be interested in.
# We use the threecommonfactors data set again:
model <- "
# Structural model
eta2 ~ eta1
eta3 ~ eta1 + eta2
# (Reflective) measurement model
eta1 =~ y11 + y12 + y13
eta2 =~ y21 + y22 + y23
eta3 =~ y31 + y32 + y33
"
### PLS vs PLSc and disattenuation
# In the model all concepts are modeled as common factors. If
# .approach_weights = "PLS-PM", csem() uses PLSc to disattenuate composite-indicator
# and composite-composite correlations.
res_plsc <- csem(threecommonfactors, model, .approach_weights = "PLS-PM")
res$Information$Model$construct_type # all common factors
# To obtain "original" (inconsistent) PLS estimates use `.disattenuate = FALSE`
res_pls <- csem(threecommonfactors, model,
.approach_weights = "PLS-PM",
.disattenuate = FALSE
)
s_plsc <- summarize(res_plsc)
s_pls <- summarize(res_pls)
# Compare
data.frame(
"Path" = s_plsc$Estimates$Path_estimates$Name,
"Pop_value" = c(0.6, 0.4, 0.35), # see ?threecommonfactors
"PLSc" = s_plsc$Estimates$Path_estimates$Estimate,
"PLS" = s_pls$Estimates$Path_estimates$Estimate
)
### Resampling --------------------------------------------------------------
# }
# NOT RUN {
## Basic resampling
res_boot <- csem(threecommonfactors, model, .resample_method = "bootstrap")
res_jack <- csem(threecommonfactors, model, .resample_method = "jackknife")
# See ?resamplecSEMResults for more examples
### Choosing a different weightning scheme ----------------------------------
res_gscam <- csem(threecommonfactors, model, .approach_weights = "GSCA")
res_gsca <- csem(threecommonfactors, model,
.approach_weights = "GSCA",
.disattenuate = FALSE
)
s_gscam <- summarize(res_gscam)
s_gsca <- summarize(res_gsca)
# Compare
data.frame(
"Path" = s_gscam$Estimates$Path_estimates$Name,
"Pop_value" = c(0.6, 0.4, 0.35), # see ?threecommonfactors
"GSCAm" = s_gscam$Estimates$Path_estimates$Estimate,
"GSCA" = s_gsca$Estimates$Path_estimates$Estimate
)
# }
# NOT RUN {
### Fine-tuning a weighting scheme ------------------------------------------
## Setting starting values
sv <- list("eta1" = c("y12" = 10, "y13" = 4, "y11" = 1))
res <- csem(threecommonfactors, model, .starting_values = sv)
## Choosing a different inner weighting scheme
#?args_csem_dotdotdot
res <- csem(threecommonfactors, model, .PLS_weight_scheme_inner = "factorial",
.PLS_ignore_structural_model = TRUE)
## Choosing different modes for PLS
# By default, concepts modeled as common factors uses PLS Mode A weights.
modes <- list("eta1" = "unit", "eta2" = "modeB", "eta3" = "unit")
res <- csem(threecommonfactors, model, .PLS_modes = modes)
summarize(res)
# }
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