![]() ![]() How to set the graphical parameters of your plots with the ggbiplot package!.You'll also see how you can get started on interpreting the results of these visualizations and.Next, you'll use the results of the previous section to plot your first PCA - Visualization is very important!.Then, you'll try a simple PCA with a simple and easy-to-understand data set.You'll first go through an introduction to PCA: you'll learn about principal components and how they relate to eigenvalues and eigenvectors.More specifically, you'll tackle the following topics: "wide" datasets, where you have many variables for each sample. It is particularly helpful in the case of Principal Component Analysis (PCA) is a useful technique for exploratoryĭata analysis, allowing you to better visualize the variation present inĪ dataset with many variables. ![]()
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