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.
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)