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I've been using some packages in R for MCA/PCA-like analysis, yet I can't seem to find a package (or a function) within a package which allows me to implement the Kaiser-Guttman Criterion for specifying all the eigenvalues greater than 1.

Would anybody know a command in R for doing so, and the relevant packages?

There is a command at the very bottom of this page, but the authors do not specify the package used in R.

https://www.geo.fu-berlin.de/en/v/soga/Geodata-analysis/Principal-Component-Analysis/principal-components-basics/Choose-principal-components/index.html

Would be good to hear everyone's thoughts.

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If you have the eigenvalues in a single vector you can simply use base R commands to only select values greater than one. The linked page uses the following commmand:

food.pca.eigen$values[food.pca.eigen$values >=1]

which simply uses base R syntax. food.pca.eigen$values >=1 returns a true/false vector depending on if the value is greater than or equal to 1 or not, which is then used as a mask for the food.pca.eigen vector. The only values selected are the ones where the mask has the value of true, which are then ones with a value equal to or greater than 1.

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  • $\begingroup$ Thank you @Oxbowerce $\endgroup$
    – EB3112
    Mar 30, 2021 at 10:23

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