![]() ![]() These new variables corresponds to a linear combination of the originals and are called principal components. The goal of principal component analysis is to transform the initial variables into a new set of variables which explain the variation in the data. If you have more than 3 variables in your data sets, it could be very difficult to visualize a multi-dimensional hyperspace. Each variable could be considered as a different dimension. Principal component analysis (PCA) allows us to summarize the variations (informations) in a data set described by multiple variables. Visualize supplementary quantitative variables. ![]() Principal component analysis using supplementary individuals and variables.Change the color of individuals by groups.Graph of individuals using FactoMineR base graph.Contribition of individuals to the princial components.Cos2 : quality of representation of individuals on the principal components.Coordinates of individuals on the principal components.Graph of variables using FactoMineR base graph.Contributions of the variables to the principal components.Cos2 : quality of variables on the factor map.Coordinates of variables on the principal components.Variables factor map : The correlation circle. ![]()
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