Composite-Based Structural Equation Modeling
Internal: Multiple testing correction
Complete list of assess()'s ... arguments
Show argument defaults or candidates
Assess model
Internal: Build DOT code for the SEM plot, including construct correla...
Internal: Second/Third stage of the two-stage approach for second orde...
Average variance extracted (AVE)
Internal: Calculate composite variance-covariance matrix
Internal: Calculate construct variance-covariance matrix
Internal: Calculate PLSc correction factors
Degrees of freedom
Internal: Matrix difference
Internal: Calculate direct, indirect and total effect
Calculate Cohen's f^2
Fornell-Larcker criterion
Internal: ANOVA F-test statistic
Goodness of Fit (GoF)
HTMT
Internal: Calculate indicator correlation matrix
Internal: Calculate the inner weights for PLS-PM
Internal: Calculate prediction metrics
Model selection criteria
Internal: Calculate the outer weights for PLS-PM
Internal: Parameter differences across groups
Internal: Calculation of the CDF used in Henseler et al. (2009)
Relative Goodness of Fit (relative GoF)
Internal: Calculate Reliabilities
Calculate variance inflation factors (VIF) for weights obtained by PLS...
Calculate composite weights using GSCA
Calculate weights using GSCAm
Calculate composite weights using GCCA
Calculate composite weights using principal component analysis (PCA)
Calculate composite weights using PLS-PM
Calculate composite weights using unit weights
Internal: Check whether two indicators belong to the same construct.
Internal: Check convergence
Internal: Classify structural model terms by type
Internal: Clean a node name.
Internal: Convert second order cSEMModel
cSEMArguments
cSEMModel
cSEMResults
cSEMSummarize
cSEMTest
cSEM: A package for composite-based structural equation modeling
Composite-based SEM
Calculate difference between S and Sigma_hat
Do an importance-performance matrix analysis
Do a nonlinear effects analysis
Do a redundancy analysis
Internal: Estimate the structural coefficients
Export to Excel (.xlsx)
Internal: firstOrderMeasurementEdges
Model fit measures
Model-implied indicator or construct variance-covariance matrix
Internal: Composite-based SEM
Internal: get significance stars
Get construct scores
Internal: Parameter names
Internal: Extract relevant parameters from several cSEMResults_multi
Internal: Helper for doNonlinearEffectsAnalysis()
Internal: Handle arguments
Inference
Internal: Helper for infer()
Internal: Calculate consistent moments of a nonlinear model
Internal: Utility functions for the estimation of nonlinear models
Parse lavaan model
cSEMIPMA
method for plot()
cSEMNonlinearEffects
method for plot()
cSEMPredict
method for plot()
cSEMResults
method for plot()
for second-order models.
cSEMResults
method for plot()
cSEMResults
method for plot()
for multiple groups.
Predict indicator scores
cSEMAssess
method for print()
cSEMNonlinearEffectsAnalysis
method for print()
cSEMPlotPredict
method for print()
cSEMPredict
method for print()
cSEMResults
method for print()
cSEMSummarize
method for print()
cSEMTestCVPAT
method for print()
cSEMTestHausman
method for print()
cSEMTestMGD
method for print()
cSEMTestMICOM
method for print()
cSEMTestOMF
method for print()
cSEMVerify
method for print()
Internal: Process data
Reliability
Resample cSEMResults
Resample data
Internal: save_single_plot Helper function to save a single DiagrammeR...
savePlot
Internal: Scale weights
Internal: secondOrderMeasurementEdges
Internal: Set the dominant indicator
Internal: Set starting values
Internal: get structured cSEMTestMGD results
Summarize model
Perform a Cross-Validated Predictive Ability Test (CVPAT)
Regression-based Hausman test
Tests for multi-group comparisons
Test measurement invariance of composites
Test for overall model fit
Verify admissibility
Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).
Useful links