# findElbowPoint: Find the elbow point in the curve of variance explained by... In PCAtools: PCAtools: Everything Principal Components Analysis

## 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

 `1` ```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.

Aaron Lun

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ``` 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") ```

PCAtools documentation built on Nov. 8, 2020, 8:17 p.m.