Build Status CRAN_Status_Badge metacran downloads

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:

Documentation

The vignette for Seminr can be found in the CRAN 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 including any interactions or higher order constructs:

# Distinguish and mix composite or reflective (common-factor) measurement models
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)),
  composite("Quality",      multi_items("PERQ", 1:7)),
  composite("Complaints",   single_item("CUSCO")),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = orthogonal),
  higher_composite("Value", dimensions = c("Quality","Complaints"), method = two_stage, weights = mode_B)
)

2 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","Image*Value"), 
        to = "Loyalty")
)

3 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, 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