![]() The eigenvectors retrieved by the function eigen are normalized. This sum of squares is maximized in the first principal component. Regarding to the eigenvalues, their numeric values are equal to the sum of squares of the distances of each projected data point in the corresponding principal component. Note that eigenvectors are perpendicular: # Multiply both eigenvectors Geom_abline(slope = ev2_m, color = "red", size = 0.7) +Īs you can see, there is an eigenvector for each variable in the data set, at this case two. Geom_abline(slope = ev1_m, color = "blue", size = 0.7) + # Scatter plot showing the span of both eigenvectors The span of each eigenvector can be considered that “line” that capture most variation: # First eigenvector These directions can be obtained through calculate the eigenvalues and eigenvectors from the covariance matrix: # Use eigen() to obtain eigenvectors and eigenvalues They are “lines” that capture most information of the data. ![]() Principal components represent the directions of the data that explain the maximal amount of variance. Obtain the eigenvalues and eigenvectors from the covariance matrix
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