![]() The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. If the original fit used a formula or a data frame or a matrix with column names, newdata must contain columns with the same. This said, PC1 and PC2, by the very nature of PCA, are indeed usually the most important parts of a PCA analysis. ![]() However, PCA is much more than the bi-plot and much more than PC1 and PC2. ![]() Principal component analysis ( PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The bi-plot comparing PC1 versus PC2 is the most characteristic plot of PCA.
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