Domain-Specific Language for Building PLS Structural Equation Models

A powerful, easy to write and easy to modify syntax for specifying and estimating Partial Least Squares (PLS) path models allowing for the latest estimation methods for Consistent PLS as per Dijkstra & Henseler (2015, MISQ 39(2): 297-316), adjusted interactions as per Henseler & Chin (2010) and bootstrapping utilizing parallel processing as per Hair et al. (2017, ISBN:978-1483377445).

SEMinR brings many advancements to creating and estimating structural equation models (SEM) using Partial Least Squares Path Modeling (PLS-PM):

• A natural feeling, domain-specific language to build and estimate structural equation models in R
• Uses variance-based PLS estimation to model both composite and common-factor constructs
• High-level functions to quickly specify interactions and complicated structural models

SEMinR follows the latest best-practices in methodological literature:

• Automatically adjusts PLS estimates to ensure consistency (PLSc) wherever common factors are involved
• Ajusts for known biases in interaction terms in PLS models
• Continuously tested against leading PLSPM software to ensure parity of outcomes: SmartPLS (Ringle et al., 2015) and ADANCO (Henseler and Dijkstra, 2015), as well as other R packages such as semPLS (Monecke and Leisch, 2012) and matrixpls (Rönkkö, 2016)
• High performance, multi-core bootstrapping function

Documentation

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-contained"), demo("seminr-ecsi") or demo("seminr-interaction") commands after installation.

Installation

You can install SEMinR with:

install.packages("seminr")

Usage

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 constructs
intxns <- 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 constructs
structure <- 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 parts
pls_model <- estimate_pls(data = mobi, measurements, intxns, structure)
summary(pls_model)
 
# Use multi-core parallel processing to speed up bootstraps
boot_estimates <- bootstrap_model(pls_model, nboot = 1000, cores = 2)
summary(boot_estimates)

Authors

• Soumya Ray
• Nicholas Danks