findElbowPoint: Find the elbow point in the curve of variance explained by...

In kevinblighe/PCAtools: PCAtools: Everything Principal Components Analysis

Find the elbow point in the curve of variance explained by each successive PC. This can be used to determine the number of PCs to retain.

Description

Find the elbow point in the curve of variance explained by each successive PC. This can be used to determine the number of PCs to retain.

Usage

findElbowPoint(variance)

Arguments

variance

Numeric vector containing the variance explained by each PC. Should be monotonic decreasing.

Details

Find the elbow point in the curve of variance explained by each successive PC. This can be used to determine the number of PCs to retain.

Value

An integer scalar specifying the number of PCs at the elbow point.

Author(s)

Aaron Lun

Examples

  col <- 20
  row <- 1000
  mat <- matrix(rexp(col*row, rate = 1), ncol = col)

  # Adding some structure to make it more interesting.
  mat[1:100,1:3] <- mat[1:100,1:3] + 5
  mat[1:100+100,3:6] <- mat[1:100+100,3:6] + 5
  mat[1:100+200,7:10] <- mat[1:100+200,7:10] + 5
  mat[1:100+300,11:15] <- mat[1:100+300,11:15] + 5

  p <- pca(mat)
  chosen <- findElbowPoint(p$variance)

  plot(p$variance)
  abline(v=chosen, col="red")

kevinblighe/PCAtools documentation built on Oct. 22, 2023, 12:01 p.m.