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What is PCA in R: prcomp vs princomp Guide about?

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of large data sets. There are two main methods for performing PCA in R: using the princomp() function, which uses spectral decomposition, and the prcomp() function, which uses singular value decomposition. Both functions output the principal components, loadings, explained variances, and scores. The factoextra package can be used to visualize PCA results in R. The tutorial demonstrates how to conduct PCA using prcomp() on sa

Author

Cleaver Bright

Language

EN