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Principal component analysis (PCA) can be seen as a singular value decomposition (SVD) of a column-centred data matrix. In a number of applications, no pre-processing of the data is carried out, and it is the uncentred data matrix that is subjected to an SVD, in what is often called an uncentred PCA. This paper explores the relationships between the results(More)
The subselect package addresses the issue of variable selection in different statistical contexts, among which exploratory data analyses; univariate or multivariate linear models; generalized linear models; principal components analysis; linear discriminant analysis, canonical correlation analysis. Selecting variable subsets requires the definition of a(More)
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