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Are there any good resources for learning how to construct structural equation models in R? A friend asked for help transitioning from SPSS' Amos for structural equation modeling to R. He has limited R skills and I have limited SEM knowledge. Are there any books/book chapters/etc along the lines of the Use R! series that cover SEM packages for R?
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The sem package provides basic structural equation modeling facilities in R, including the ability to fit structural equations in observed variable models by two-stage least squares, and to fit latent variable models by full information maximum likelihood as- suming multinormality.
But yes SEM takes much practice and a core statistician to come up with a statistically valid results (Also need to take care of statistical interpretation of results). & Yes you can say that it's comparatively a bit complex technique with standard structure of equations( which needs to address correctly).
Structural equation modeling is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs.
Structural Equation Modeling (SEM)is quantitative research technique that can also incorporates qualitative methods. SEM is used to show the causal relationships between variables. The relationships shown in SEM represent the hypotheses of the researchers.
A quick google search reveals:
The following packages on CRAN may be helpful:
sem (structural equation modelling) that contains links to the package author's website
lavaan (latent variable analyis)
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With reference to Structural Equation Modelling, the theory is the hard part. If your friend has that, then he should be OK.
I personally like OpenMx (even though its not completely open source), as it is fast, has parallel facilities and can handle an extremely wide variety of models though coding the algebra by hand.
The user guide is extremely good: http://openmx.psyc.virginia.edu/documentation
One issue with it is the use of Graphviz for diagrams, which could cause problems if you are running it on Windows. Lavaan and sem are also quite good, but the optimisers tend to fail on badly behaved matrcies where OpenMx can handle it. Whether you regard that as a good or bad thing is a matter of preference.
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