Principal Component Analysis

Practicing Statistics Interview Questions in R

Zuzanna Chmielewska

Actuary

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

prcomp()

pca <- prcomp(~ v1 + v2 + v3, data = df)
predict(pca)

pca <- prcomp(~ v1 + v2 + v3, data = df)
predict(pca)
summary(pca)

pca <- prcomp(~ v1 + v2 + v3, data = df, rank = 2)
predict(pca)
summary(pca)

pca <- prcomp(~ v1 + v2 + v3, data = df, tol = 0.25)
predict(pca)
summary(pca)

Omitted if $ \sigma_{PCi} \leq tol \cdot \sigma_{PC1} $

Summary

• application of PCA
• rotation of axes in PCA
• PCA in R: prcomp()

Let's practice!

Practicing Statistics Interview Questions in R