Seminr
Natural feeling domain-specific language for building structural equation models in R for estimation by covariance-based methods (like LISREL/Lavaan) or partial least squares (like SmartPLS)
Install / Use
/learn @sem-in-r/SeminrREADME
SEMinR allows users to easily create and modify structural equation models (SEM). It allows estimation using either covariance-based SEM (CBSEM, such as LISREL/Lavaan), or Partial Least Squares Path Modeling (PLS-PM, such as SmartPLS/semPLS).
First Look
Main features of using SEMinR:
- A natural feeling, domain-specific language to build and estimate SEMs in R
- High-level functions to quickly specify interactions and complicated structural models
- Modular design of models that promotes reuse of model components
- Encourages best practices by use of smart defaults and warnings
Take a look at the easy syntax and modular design:
# Define measurements with famliar terms: reflective, composite, multi-item constructs, etc.
measurements <- constructs(
reflective("Image", multi_items("IMAG", 1:5)),
composite("Expectation", multi_items("CUEX", 1:3)),
composite("Loyalty", multi_items("CUSL", 1:3), weights = mode_B),
composite("Complaints", single_item("CUSCO"))
)
# Create four relationships (two regressions) in one line!
structure <- relationships(
paths(from = c("Image", "Expectation"), to = c("Complaints", "Loyalty"))
)
# Estimate the model using PLS estimation scheme (Consistent PLS for reflectives)
pls_model <- estimate_pls(data = mobi, measurements, structure)
#> Generating the seminr model
#> All 250 observations are valid.
# Re-estimate the model as a purely reflective model using CBSEM
cbsem_model <- estimate_cbsem(data = mobi, as.reflective(measurements), structure)
#> Generating the seminr model for CBSEM
SEMinR can plot models using the semplot (for CBSEM models) or
DiagrammeR (for PLS models) packages with a simple plot method.
plot(pls_model, title = "PLS Model")
save_plot("myfigure.pdf")
SEMinR allows various estimation methods for constructs and SEMs:
- Covariance-based Structural Equation Modeling (CBSEM)
- Covariance-based estimation of SEM using the popular Lavaan package
- Currently supports mediation and moderation models with constructs
- Easily specify interactions between constructs
- Adds ten Berge factor score extraction to get same correlation patterns as latent factors
- Adds VIF and other validity assessments
- Confirmatory Factor Analysis (CFA) using Lavaan
- Uses Lavaan package and returns results and syntax
- Adds ten Berge factor score extraction to get same correlation patterns as latent factors
- Partial Least Squares Path Modeling (PLS-PM)
- Uses non-parametric variance-based estimation to construct composites and common-factors
- Automatically estimates using Consistent PLS (PLSc) when emulating reflective common factors
- Adjusts for known biases in interaction terms in PLS models
- Continuously tested against leading PLS-PM software to ensure parity of outcomes: SmartPLS, ADANCO, semPLS, and matrixpls
- High performance, multi-core bootstrapping function
Researchers can now create a SEM and estimate it using different techniques (CBSEM, PLS-PM).
Installation
You can install SEMinR from R with:
install.packages("seminr")
Usage and Examples
Load the SEMinR package:
library(seminr)
Describe measurement and structural models and then estimate them. See the various examples below for different use cases:
- CFA + CBSEM Example with Common Factors
- Consistent-PLS (PLSc) Example with Common Factors
- PLS-PM Example with Composites
- Comparing CBSEM and PLS-PM Example
CFA + CBSEM Example with Common Factors
Note that CBSEM models reflective common-factor constructs, not composites. SEMinR uses the powerful Lavaan package to estimate CBSEM models – you can even inspect the more complicated Lavaan syntax that is produced.
Describe reflective constructs and interactions:
# Distinguish and mix composite or reflective (common-factor) measurement models
# - composite measurements will have to be converted into reflective ones for CBSEM (see below)
measurements <- constructs(
reflective("Image", multi_items("IMAG", 1:5)),
reflective("Expectation", multi_items("CUEX", 1:3)),
interaction_term(iv = "Image", moderator = "Expectation", method = two_stage),
reflective("Loyalty", multi_items("CUSL", 1:3)),
reflective("Complaints", single_item("CUSCO"))
)
Describe the 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"),
paths(from = "Image", to = c("Complaints"))
)
Put the above elements together to estimate the model using Lavaan:
# Evaluate only the measurement model using Confirmatory Factor Analysis (CFA)
cfa_model <- estimate_cfa(data = mobi, measurements)
summary(cfa_model)
# Dynamically compose full SEM models from individual parts
# - if measurement model includes composites, convert all constructs to reflective using:
# as.reflective(measurements)
cbsem_model <- estimate_cbsem(data = mobi, measurements, structure)
sum_cbsem_model <- summary(cbsem_model)
sum_cbsem_model$meta$syntax # See the Lavaan syntax if you wish
Consistent-PLS (PLSc) Example with Common Factors
Models with reflective common-factor constructs can also be estimated in PLS-PM, using Consistent-PLS (PLSc). Note that the popular SmartPLS software models constructs as composites rather than common-factors (see below) but can also do PLSc as a special option.
We will reuse the measurement and structural models from earlier:
# Optionally inspect the measuremnt and structural models
measurements
structure
Estimate full model using Consistent-PLS and bootstrap it for confidence intervals:
# Models with reflective constructs are automatically estimated using PLSc
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)
PLS-PM Example with Composites
PLS-PM typically models composites (constructs that are weighted average of items) rather than common factors. Popular software like SmartPLS models composites either as Mode A (correlation weights) or Mode B (regression weights). We also support both modes as well as second-order composites. rather than common factors. Popular software like SmartPLS models composites by default, either as Mode A (correlation weights) or Mode B (regression weights). We also support second-order composites.
Describe measurement model for each composite, interaction, or higher order composite:
# Composites are Mode A (correlation) weighted by default
mobi_mm <- constructs(
composite("Image", multi_items("IMAG", 1:5)),
composite("Value", multi_items("PERV", 1:2)),
higher_composite("Satisfaction", dimensions = c("Image","Value"), method = two_stage),
composite("Expectation", multi_items("CUEX", 1:3)),
composite("Quality", multi_items("PERQ", 1:7), weights = mode_B),
composite("Complaints", single_item("CUSCO")),
composite("Loyalty", multi_items("CUSL", 1:3), weights = mode_B)
)
Define a structural (inner) model for our PLS-PM:
mobi_sm <- relationships(
paths(from = c("Expectation","Quality"), to = "Satisfaction"),
paths(from = "Satisfaction", to = c("Complaints", "Loyalty"))
)
Estimate full model using PLS-PM and bootstrap it for confidence intervals:
pls_model <- estimate_pls(
data = mobi,
measurement_model = mobi_mm,
structural_model = mobi_sm
)
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)
Plotting the model results
SEMinR can plot all supported models using the dot language and the
graphViz.js widget from the DiagrammeR package.
# generate a small model for creating the plot
mobi_mm <- constructs(
composite("Image", multi_items("IMAG", 1:3)),
composite("Value", multi_items("PERV", 1:2)),
higher_composite("Satisfaction", dimensions = c("Image","Value"), method = two_stage),
composite("Quality", multi_items("PERQ", 1:3), weights = mode_B),
composite("Complaints", single_item("CUSCO")),
reflective("Loyalty", multi_items("CUSL", 1:3))
)
mobi_sm <- relationships(
paths(from = c("Quality"), to = "Satisfaction"),
paths(from = "Satisfaction", to = c("Complaints", "Loyalty"))
)
pls_model <- estimate_pls(
data = mobi,
measurement_model = mobi_mm,
structural_model = mobi_sm
)
#> Generating the seminr model
#> Generating the seminr model
#> All 250 observations are valid.
#> All 250 observations are valid.
boot_estimates <- bootstrap_model(pls_model, nboot = 100, cores = 1)
#> Bootstrapping model using seminr...
#>
