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cbsem (version 1.0.0)

Simulation, Estimation and Segmentation of Composite Based Structural Equation Models

Description

The composites are linear combinations of their indicators in composite based structural equation models. Structural models are considered consisting of two blocks. The indicators of the exogenous composites are named by X, the indicators of the endogenous by Y. Reflective relations are given by arrows pointing from the composite to their indicators. Their values are called loadings. In a reflective-reflective scenario all indicators have loadings. Arrows are pointing to their indicators only from the endogenous composites in the formative-reflective scenario. There are no loadings at all in the formative-formative scenario. The covariance matrices are computed for these three scenarios. They can be used to simulate these models. These models can also be estimated and a segmentation procedure is included as well.

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Version

Install

install.packages('cbsem')

Monthly Downloads

2

Version

1.0.0

License

GPL

Maintainer

Rainer Schlittgen

Last Published

February 3rd, 2019

Functions in cbsem (1.0.0)

Checking composite based SE models if there are weights in accordance with the loadings and the covariance matrix of the composites

Determination of the covariance matrix of a GSC model belonging to scenario 1, scenario 2, scenario 3

Output of covgscmodel for the simplemodel data.

gscmcovrr determines the covariance matrix of a GSC model belonging to scenario rr.

SolveCorr Solve the Vale-Maurelli cubic equation to find the intermediate correlation between two normal variables that gives rise to a target correlation (rho) between the two transformed nonnormal variables.

VMTargetCorr Given a target correlation matrix, R, and target values of skewness and kurtosis for each marginal distribution, find the "intermediate" correlation matrix, V

Mobile phone data for the ECSI model.

Simulated data.

Functions to generate nonnormal distributed multivariate random vectors with mean=0, var=1 and given correlations and coefficients of skewness and excess kurtosis. This is done with the method of Vale & Morelli: The coefficients of the Fleishman transform Y = -c + bX +cX^2 + dX^3 are computed. from given skewness gamma[1] = E(Y^3) and kurtosis gamma[2] = E(Y^4) - 3. A indermediate correlation matrix is computed from the desired correlation matrix and the Fleishman coefficients. A singular value decomposition of the indermediate correlation matrix is performed and a matrix of independend normal random numbers is generated and transformed into correlated ones. Finally the Fleishman transform is applied to the columns of this data matrix.

NewtonFl Newton's method to find roots of the function FlFunc.

Estimation of pls-path models

Estimating GSC models belonging to scenarios reflective-reflective, formative-reflective and formative-formative

Output of gscals for the simplemodel data.

Simulated data.

Function for use in checkw

gscmcovff determines the covariance matrix of a GSC model belonging to scenario ff.

gscmcovfr determines the covariance matrix of a GSC model belonging to scenario fr. The covariance matrices of the errors are supposed to be diagonal.

For use in boottestgscm.

Testing two segmentations of a GSC model

Political and economical freedom.

rValeMaurelli Simulate data from a multivariate nonnormal distribution such that 1) Each marginal distribution has a specified skewness and kurtosis 2) The marginal variables have the correlation matrix R

FlDerivcompute the Jacobian of the Fleishman transform for a given set of coefficients b,c,d

clustergscairls

Clustering gsc-models

For use in clustergscairls, residuals of a gsc-model

Fleishman computes the variance, skewness and kurtosis for a given set of of coefficients b,c,d for the Fleishman transform