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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)
Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new(More)
BACKGROUND Dry matter degradability (DMD) parameters (a, b and c in the Ørskov and McDonald model) are usually determined by the nylon bag technique. The aim of this study was to estimate DMD parameters of ruminant mixed diets, which are in general unavailable, through multiple linear regressions on their chemical composition (ash, crude protein, neutral(More)
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