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SEMinR allows users to easily create and modify structural equationmodels (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
• Installation
• Usage and Examples
• Documentation
• Partner Projects
• Contact Us
• About Us

Main features of using SEMinR:

• Anatural feeling,domain-specific language to build and estimateSEMs in R
• High-level functions to quickly specify interactions and complicatedstructural models
• Modular design of models that promotes reuse of model components
• Encouragesbest 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 CBSEMcbsem_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) orDiagrammeR (for PLS models) packages with a simpleplot 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 popularLavaan package
  • Currently supports mediation and moderation models with constructs
  • Easilyspecify interactions between constructs
  • Adds ten Bergefactor score extraction to get same correlationpatterns as latent factors
  • AddsVIF and other validity assessments
• Confirmatory Factor Analysis (CFA) using Lavaan
  • UsesLavaan package andreturns results and syntax
  • Adds ten Bergefactor score extraction to get same correlationpatterns as latent factors
• Partial Least Squares Path Modeling (PLS-PM)
  • Uses non-parametricvariance-based estimation to constructcomposites andcommon-factors
  • Automaticallyestimates using Consistent PLS (PLSc) when emulatingreflective common factors
  • Adjusts for known biases in interaction terms in PLS models
  • Continuously tested against leading PLS-PM software to ensure parityof outcomes: SmartPLS, ADANCO, semPLS, and matrixpls
  • High performance, multi-core bootstrapping function

Researchers can now create a SEM and estimate it using differenttechniques (CBSEM, PLS-PM).

You can install SEMinR from R with:

install.packages("seminr")

Load the SEMinR package:

library(seminr)

Describe measurement and structural models and then estimate them. Seethe various examples below for different use cases:

1. CFA + CBSEM Example with CommonFactors
2. Consistent-PLS (PLSc) Example with CommonFactors
3. PLS-PM Example with Composites
4. Comparing CBSEM and PLS-PMExample

Note that CBSEM models reflective common-factor constructs, notcomposites. SEMinR uses the powerful Lavaan package to estimate CBSEMmodels – you can even inspect the more complicated Lavaan syntax that isproduced.

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 constructsstructure<- 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

Models with reflective common-factor constructs can also be estimated inPLS-PM, using Consistent-PLS (PLSc). Note that the popular SmartPLSsoftware models constructs as composites rather than common-factors(see below) but can also do PLSc as aspecial option.

We will reuse the measurement and structural models from earlier:

# Optionally inspect the measuremnt and structural modelsmeasurementsstructure

Estimate full model using Consistent-PLS and bootstrap it for confidenceintervals:

# Models with reflective constructs are automatically estimated using PLScpls_model<- estimate_pls(data=mobi,measurements,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)

PLS-PM typically models composites (constructs that are weighted averageof items) rather than common factors. Popular software like SmartPLSmodels composites either as Mode A (correlation weights) or Mode B(regression weights). We also support both modes as well as second-ordercomposites. rather than common factors. Popular software like SmartPLSmodels composites by default, either as Mode A (correlation weights) orMode B (regression weights). We also support second-order composites.

Describe measurement model for each composite, interaction, or higherorder composite:

# Composites are Mode A (correlation) weighted by defaultmobi_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 confidenceintervals:

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 bootstrapsboot_estimates<- bootstrap_model(pls_model,nboot=1000,cores=2)summary(boot_estimates)

SEMinR can plot all supported models using the dot language and thegraphViz.js widget from theDiagrammeR package.

# generate a small model for creating the plotmobi_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...#> SEMinR Model successfully bootstrapped

When we have a model, we can plot it and save the plot to files.

plot(boot_estimates,title="Bootstrapped Model")save_plot("myfigure.pdf")

We can customize the plot using an elaborate theme. Themes can be usedfor individual plots as a parameter or set as a default. Using theseminr_theme_create() function allows to define different themes.

# Tip: auto complete is your friend in finding all possible themeing options.thm<- seminr_theme_create(plot.rounding=2,plot.adj=FALSE,sm.node.fill="cadetblue1",mm.node.fill="lightgray")# change new default theme - valid until R is restartedseminr_theme_set(thm)# the new plotplot(boot_estimates)

We can re-estimate a composite PLS-PM model as a common-factor CBSEM.Such a comparison might interest researchers seeking to evaluate howtheir constructs behave when modeled as composites versuscommon-factors.

# Define measurements with famliar terms: reflective, multi-item constructs, etc.measurements<- constructs(  composite("Image",       multi_items("IMAG",1:5)),  composite("Expectation", multi_items("CUEX",1:3)),  composite("Loyalty",     multi_items("CUSL",1:3)),  composite("Complaints",  single_item("CUSCO")))# Create four relationships (two regressions) in one line!structure<- relationships(  paths(from= c("Image","Expectation"),to= c("Complaints","Loyalty")))# First, estimate the model using PLSpls_model<- estimate_pls(data=mobi,measurements,structure)# Reusable parts of the model to estimate CBSEM results# note: we are using the `as.reflective()` function to convert composites to common factorscbsem_model<- estimate_cbsem(data=mobi, as.reflective(measurements),structure)# Re-estimate the model using common factors in Consistent PLS (PLSc)pls_model<- estimate_pls(data=mobi, as.reflective(measurements),structure)

Thevignette for Seminr can be found onGitHub or by running thevignette("SEMinR") command after installation.

Demo code for various use cases with SEMinR can be found in theseminr/demo/folder or by running commands such asdemo("seminr-contained") afterinstallation.

Model Specification:

• seminr-cbsem-cfa-ecsi: Conductconfirmatory model building using CFA and CBSEM
• seminr-pls-ecsi: Conduct PLS path modeling
• seminr-pls-interaction: Defineinteractions between constructs in SEM models
• seminr-pls-higher_order: Definehigher-order composites for PLS models
• seminr-pls-mga: Assess structural differencesbetween subgroups using PLS-MGA
• seminr-plsc-ecsi: Run PLSc to emulatecommon factors using in PLSc

Model Visualization:

• seminr-dot-graph: Create a plot from aSEM model

Syntax Style:

• seminr-alternative-models: Reusemeasurement and structural components in multiple models
• seminr-style-contained: Create andexecute a SEM model in one function call

• seminrstudio: A set ofaddins for RStudio to simplify using SEMinR.

We communicate and collaborate with several other open-source projectson SEM related issues.

• plspm package for R: an earlyand limited PLS path modeling package for R that inspired thedevelopment of SEMinR, among others; it is no longer maintained.
• plspm package forPython: awell-maintained PLS modeling pakage for Python; it is tested againstSEMinR and borrows some syntactic ideas from SEMinR.
• cSEM: a well-maintained andcomprehensive composite analysis project implementing PLS and GSCA forR, using Lavaan style syntax

Facebook Group:https://www.facebook.com/groups/seminr

You will find the developers and other users here who might also be ableto help or discuss.

Issue Tracker:https://github.com/sem-in-r/seminr/issues

This is the official place to submit potential bugs or request newfeatures for consideration.

Primary Authors:

• Soumya Ray
• NicholasDanks
• André Calero Valdez

Key Contributors:

• James Uanhoro (ten Berge factorextraction, advice on covariance-based methods)
• Arturo Heynar CanoBejar(evaluation and testing of PLS and CBSEM models)
• Johannes Nakayama(contributions to the model visualization functionality)

And many thanks to the growing number of folks who have reached out withfeature requests, bug reports, and encouragement. You keep us going!