Biplot for PCA Explained

Biplot is a type of scatterplot used in PCA. In this special plot, the original data is represented by principal components that explain the majority of the data variance using the loading vectors and PC scores.

In this tutorial, you’ll learn how to interpret the biplots in the scope of PCA.

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This page was created in collaboration with Paula Villasante Soriano and Cansu Kebabci. Please have a look at Paula’s and Cansu’s author pages to get further information about their academic backgrounds and the other articles they have written for Statistics Globe.

Let’s dive right in!

Example Data

For demonstration, the iris dataset is used. The dataset contains the measurements of sepal length and width, and petal length and width in centimeters for 50 samples of each of three Iris flower species: Iris Setosa, Versicolor, and Virginia. Let’s take a quick look at the first six rows of the dataset!

Perform PCA

The next step is to perform the PCA to get the principal component scores and loadings that will be used in the biplot. As we only focus on the interpretation of biplots in this tutorial, the content is limited to the statistical explanation of biplots. For further information on the theoretical background of PCA please see our tutorial PCA Explained and regarding the programming, you can visit our other tutorials: PCA in R and PCA in Python.

The principal component scores and loadings for the first two principal components are given in Tables 2 and 3 below. If you wonder why it is only the first two components, see our tutorials Choose Optimal Number of Components for PCA and Scree Plot for PCA explaining the relationship between the components and explained variance theoretically and visually.

Table 2 shows only the first six rows of the complete table of scores since the complete table with 150 rows is huge to fit here.

In Table 3, you can see the loadings, in other words, weights that show the association between the original variables and principal components.

Visualize & Interpret PCA Results via Biplot

As early mentioned, biplots have two components: scores and loading vectors. So far, we perform the PCA and extract the component scores and loadings. Now it is time to use the extracted data shown in Tables 2 and 3 to plot a biplot to interpret the results. For information on how to plot biplots in R and Python please see our tutorials: Biplot in R and Biplot in Python.

In the biplot below, each point represents a sample of an iris flower. The axes show the principal component scores, and the vectors are the loading vectors, whose components are in the magnitudes of the loadings in Table 3. Each loading vector represents the loading pair per original variable.

You can see that the flowers that belong to the same species are located closer to each other, which indicates that they have similar principal component scores. More specifically, Setosa species have relatively lower PC1 scores, Versicolar species have somewhat higher PC2 scores and Virginica species have rather higher PC1 scores.

Okay, but what do low and high scores of PC1 or PC2 refer to? There come the loading vectors! One can take the vector projections on the principal component axes to interpret the direction and the strength of the association among the variable-component pairs. The projections on the PC1 axis guide in the relations with PC1 whereas the projections on the PC2 axis show the associations with PC2.

For instance, if one takes the projection on the PC1 axis, it can simply be said that the petal length, sepal length and petal width are in the same direction as PC1, hence they are positively correlated with PC1. On the other hand, the sepal width is in the opposite direction with PC1, which implies a negative association. Therefore, simply say, PC1 represents having smaller sepal widths and larger sepal lengths. One can also add the information: being larger in petal measurements.

By the same reasoning, PC2 represents being lower in all measurements at different levels for each width and length measurement. For example, the magnitudes of the projections of the petal vectors are negligible compared to the sepal measurements. Therefore, it is even better to only say that PC2 refers to having lower sepal measurements.

Thus, for the iris dataset, we can summarise the results in Table 4 below.

In Table 4, S. Sep-W./L. Sep-L. refers to small sepal width and large sepal length; L. Sep-W./S. Sep-L. refers to large sepal width and small sepal length; S. Sep. refers to small sepals (length and width); L. Sep. refers to large sepals.

Accordingly, Setosa differs from the other two species by its large sepal widths and small sepal lengths; Versicolar is identified by its small sepal widths and lengths; Virginica is distinguished by its small sepal widths and large sepal lengths.

As you see, it is fun to interpret biplots! Would you be interested in finding your favorite pizza type via PCA? Then check the PCA in Practice section in the PCA Explained tutorial.

Video, Further Resources & Summary

Do you need more explanations on the theoretical background of a PCA? Then you should have a look at the following YouTube video of the Statistics Globe YouTube channel.

Additionally, you could have a look at some of the other tutorials on Statistics Globe:

• Principal Component Analysis (PCA) Explained
• Principal Component Analysis (PCA) in R
• Principal Component Analysis in Python
• Choose Optimal Number of Components for PCA/li>
• Scree Plot for PCA Explained
• Draw Biplot of PCA in Python
• Biplot of PCA in R

This post has shown how to interpret biplots in PCA. In case you have questions or comments, you can write them below.