A powerful, easy to syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).
SEMinR brings many advancements to creating and estimating structural equation models (SEM) using Partial Least Squares Path Modeling (PLS-PM):
SEMinR follows the latest best-practices in methodological literature:
The vignette for Seminr can be found in the seminr/inst/doc/ folder or by running the
vignette("SEMinR") command after installation.
Demo code for use of Seminr can be found in the seminr/demo/ folder or by running the
demo("seminr-interaction") commands after installation.
You can install SEMinR with:
Briefly, there are four steps to specifying and estimating a structural equation model using SEMinR:
1 Describe measurement model for each construct and its items:
measurements <- constructs(composite("Image", multi_items("IMAG", 1:5), weights = mode_B),composite("Expectation", multi_items("CUEX", 1:3), weights = mode_A),reflective("Loyalty", multi_items("CUSL", 1:3)))
2 Specify any interactions between constructs:
# Easily create orthogonalized or scaled interactions between constructsintxns <- interactions(interaction_ortho("Image", "Expectation"))
3 Describe the structural model of causal relationships between constructs (and interactions):
# Quickly create multiple paths "from" and "to" sets of constructsstructure <- relationships(paths(from = c("Image", "Expectation", "Image*Expectation"),to = "Loyalty"))
4 Put the above elements together to estimate and bootstrap the model:
# Dynamically compose SEM models from individual partspls_model <- estimate_pls(data = mobi, measurements, intxns, structure)summary(pls_model)# Use multi-core parallel processing to speed up bootstrapsboot_estimates <- bootstrap_model(pls_model, nboot = 1000, cores = 2)summary(boot_estimates)