10/31/2023 0 Comments Pca columns center in dataframe rIn this post I’ll show you 5 different ways to do a PCA using the following functions (with their corresponding packages in parentheses):īrief note: It is no coincidence that the three external packages ( "FactoMineR", "ade4", and "amap") have been developed by French data analysts, which have a long tradition and preference for PCA and other related exploratory techniques. In R, there are several functions from different packages that allow us to perform PCA. For this reason, PCA allows to reduce a “complex” data set to a lower dimension in order to reveal the structures or the dominant types of variations in both the observations and the variables. Principal Component Analysis ( PCA) is a multivariate technique that allows us to summarize the systematic patterns of variations in the data.įrom a data analysis standpoint, PCA is used for studying one table of observations and variables with the main idea of transforming the observed variables into a set of new variables, the principal components, which are uncorrelated and explain the variation in the data. 5 functions to do Principal Components Analysis in R Posted on June 17, 2012
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