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Results tagged with pca
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user 28820
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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Combining several variables into one outcome score: How is it done in the machine learning c...
This mentions about composite variable. http://www.r-bloggers.com/ecological-sems-and-composite-variables-what-why-and-how/. In R package lavann, you can create composite variable based on manifest va …
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