To find out different ways of plotting biplots in R please see our Biplot in R tutorial.įigure 5 shows a scree plot showing the explained variance per principal component in our PCA. Besides, the observation points will be colored in black by default. We will specify the deep sky blue color for the variables, or more specifically, for the loading vectors. The fviz_pca_biplot() function from the factoextra package can help us to build a biplot. They enable the user to understand what the components represent and each variable’s share in these representations. Biplots are used in general for this purpose. In order to understand the relation between the principal components and the original variables, a visual that displays both elements are needed. Visualisation of Component-Variable Relation You can find alternative ways of displaying the observations in 3D in our tutorial: 3D Plot of PCA in R. convert ( 0.5, 0.7, 0.5 ),įigure 3 shows the scatterplot of observations in 3D based on the first three principal components.
