cSEM

WARNING: THIS IS WORK IN PROGRESS. BREAKING CHANGES TO THE API ARE VERY LIKELY. Do not use the package before the first stable relase (which will be 0.0.1, towards the end of 2018).

Purpose

Estimate, analyse, test, and study linear and nonlinear structural equation models using composite based approaches, procedures, and tests including e.g. PLS, PLSc, GSCA, GSCAm, 2SLS estimation, several tests and typical postestimation procedures (e.g., computing a model-implied covariance or effects).

Installation

# Currently only a development version from GitHub is available:
# install.packages("devtools")
devtools::install_github("M-E-Rademaker/cSEM")

Philopsophy/Goals/Ideas

• Easy to use by non-R experts:
  • Functions csem() and cca() provide default choices for most of its arguments (similarity to the sem() and cfa() functions of the lavaan package is intended).
  • (Eventually) well documented (Vignettes, HTML output, a website, intro course(s)). Of course this may take some time!
  • There will be an extensive (non-expert) visually and didactically appealing documentation designed to make the learning curve of both the methods involved and the package as flat as possible.
  • Structured output/results that aims to be “easy”" in a sense that it is - … descriptive/verbose - … easy to export to other environments such as MS Word, Latex files etc. (exportability) - … easy to migrate from/to/between other PLS/VB/CB-based systems (lavaan, semPLS, ADANCO, SmartPLS) (this will also take a lot of time!)
  • (In the future) Intro courses, accompaning website, cheatsheets.
• The package is designed to be flexible/modular enough so that researchers developing new methods can take specific function provided by the package and alter them according to their need without working their way through a chain of other functions (naturally this will not always be possible). Modularity is largly inspired by the matrixpls package.
• Modern in a sense that the package integrates modern developments within the R community. This mainly includes ideas/recommendations/design choices that fead into the packages of the tidyverse.

Basic usage

require(cSEM)
data(satisfaction)

## Note: the opeartor "<~" tells cSEM that the construct to its left is modelled
##       as a composite.
##       the operator "=~" tells cSEM that the construct to its left is modelled
##       as a common factor.

model <- "
    # Structural model
    EXPE ~ IMAG
    QUAL ~ EXPE
    VAL  ~ EXPE + QUAL + QUAL.EXPE
    SAT  ~ IMAG + EXPE + QUAL + VAL + IMAG.IMAG
    LOY  ~ IMAG + SAT
    
    # Measurement model
    
    IMAG <~ imag1 + imag2 + imag3
    EXPE <~ expe1 + expe2 + expe3 
    QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5
    VAL  <~ val1  + val2  + val3
    SAT  =~ sat1  + sat2  + sat3  + sat4
    LOY  =~ loy1  + loy2  + loy3  + loy4
    "

a <- csem(.data = satisfaction, .model = model)
a

## Access elements using `$`. E.g.
a$Estimates$Loading_estimates # or
a$Information$Model

## Examine the structure:
listviewer::jsonedit(a, mode = "view") # requires the listviewer package.

## Get a summary
summarize(a) # preliminary

# Currently only `summarize()` works for nonlinear models. 

# Alter the model to obtain a linear model:

model <- "
    # Structural model
    EXPE ~ IMAG
    QUAL ~ EXPE
    VAL  ~ EXPE + QUAL
    SAT  ~ IMAG + EXPE + QUAL + VAL
    LOY  ~ IMAG + SAT
    
    # Measurement model
    
    IMAG <~ imag1 + imag2 + imag3
    EXPE <~ expe1 + expe2 + expe3 
    QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5
    VAL  <~ val1  + val2  + val3
    SAT  =~ sat1  + sat2  + sat3  + sat4
    LOY  =~ loy1  + loy2  + loy3  + loy4
    "

a <- csem(.data = satisfaction, .model = model)

## Apply postestimation functions, e.g.
verify(a) 
fit(a) # computes the model-implied indicator covariance matrix

## Test overall model fit
testOMF(a) # takes roughly 30 seconds