(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Wayne Folta writes:
I [Folta] was looking for R packages to address a project I’m working on and stumbled onto a package called ‘plspm’. It seems to be a nice package, but the thing I wanted to pass on is the PDF that Gaston Sanchez, its author, wrote that describes PLS Path Analysis in general and shows how to use plspm in particular. It’s like a 200-page R vignette that’s really informative and fun to read. I’d recommend it to you and your readers: even if you don’t want to delve into PLS and plspm deeply, the first seven pages and the Appendix A provide a great read about a grad student, PLS Path Analysis, and the history of the field.
It’s written at a more popular level than you might like. For example, he says at one point: “A moderating effect is the fancy term that some authors use to say that there is a nosy variable M influencing the effect between an independent variable X and a dependent variable Y.” You would obviously never write anything like that [yup --- AG], and most of your blog readers are pretty sophisticated.
It appears to me the PLS Path Analysis is an interesting alternative to SEM, based on partial-least-squares rather then ML. Same diagrams, similar results, similar procedures, different underlying mechanism/philosophy. And Gaston gives an interesting history of things and obviously put a lot of work into a 200+ page document and R package.
I don’t know anything about PLS path analysis but I thought I’d pass this on for the benefit of those of you who use these methods.
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