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Here is an example with the USArrests data: > data(USArrests) > library(FactoMineR) > res <- PCA(USArrests) > dimdesc(res, axes=1) # show correlation of variables with 1st axis $Dim.1 $Dim.1$quanti …

The choice between PCA and FA is a long-standing debate among psychometricians. I don't quite follow your points, though. … From a psychometrical perspectice, FA models are to be preferred since they explicitly account for measurement errors, while PCA doesn't care about that. …

The first output is the correct and most useful one. Calling loadings() on your object just returns a summary where the SS are always equal to 1, hence the % variance is just the SS loadings divided b …

You can see an example with the use of kdist() in ade4 for applying an PCA on different distance matrices. …

What is the rationale of applying an exploratory/unsupervised method (PCA or FA with VARIMAX rotation) after having tested a confirmatory model, especially if this is done on the same sample? … In your case, the estimation of your parameters will be noisy, and in the case of PCA you may expect fluctuations in your estimated loadings (just try bootstrap to get an idea of 95% CIs). …

Be careful, however, with any extra centering or scaling parameters that were applied when computing PCA EVs. In R, you may also find useful the predict() function, see ?predict.prcomp. …

You may also implement the iterative PCA yourself. …

The problem with using PCA is that (1) measurements from all of the original variables are used in the projection to the lower dimensional space, (2) only linear relationships are considered, and (3) PCA … Also, sparse PCA might be used to perform dimension reduction and variable selection based on the resulting variable loadings. …

PCA does not include an error model, which is one of the definitive characteristic of reflective measurement models: PCs are linear combinations of the observed variables, while in FA (and IRT) models … It has been shown that PCA can be rewritten with the observed variables as "dependent" variables, as in a reflective model (by Nunally and Bernstein in their Psychometric Theory textbook, IIRC), but still …

But the point is that rotation methods were originally developed in the context of the FA approach and are now routinely used with PCA. … But PCA is also used for checking the factorial structure of already validated ones. Researchers don't really matter about FA vs. …

It is basically OK to use the criteria from PCA as part of a guide to selecting the number of factors. Most of the times, FA and PCA results will be in agreement. … and Velicer's minimum average partial (MAP) are the most reliable and accurate techniques to assess the number of components or factors to retain, according to Zwick & Velicer.1 The fact that we use PCA …

This is a scaling issue, as is often the case when using these methods. You asked to scale the variables when calling pcr (scale=T), but your manual computation makes use of the raw (unscaled) data. > …

, which also points to another thread related to CCA versus PCA+regression. … Finally, I should note that there's now a unified approach to all such multi-blocks approaches ((generalized) CCA, interbattery factor analysis, redundancy analysis, hierarchical PCA , multiple co-inertia …

In this particular context, PCA is mainly used to account for population-specific variations in alleles distribution on the SNPs (or other DNA markers, although I'm only familiar with the SNP case) under … Note that any such measure of genotype distance (this also holds when using Multidimensional Scaling in place of PCA) will allow to spot relatives or siblings. …

We have a dedicated thread for that very specific purpose: Using principal component analysis (PCA) for feature selection. … If you have a "PCA" object constructed using FactoMineR::PCA, then variable contribution values are stored in the $var$contrib slot of your object. …