findCorrelation: Determine highly correlated variables
In caret: Classification and Regression Training

Description Usage Arguments Details Value Author(s) See Also Examples

This function searches through a correlation matrix and returns a vector of integers corresponding to columns to remove to reduce pair-wise correlations.

1	findCorrelation(x, cutoff = .90, verbose = FALSE)

`x`	A correlation matrix
`cutoff`	A numeric value for the pair-wise absolute correlation cutoff
`verbose`	A boolean for printing the details

The absolute values of pair-wise correlations are considered. If two variables have a high correlation, the function looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation.

There are several function in the subselect package (leaps, genetic, anneal) that can also be used to accomplish the same goal.

A vector of indices denoting the columns to remove. If no correlations meet the criteria, numeric(0) is returned.

Original R code by Dong Li, modified by Max Kuhn

leaps, genetic, anneal, findLinearCombos

corrMatrix <- diag(rep(1, 5))
corrMatrix[2, 3] <- corrMatrix[3, 2] <- .7
corrMatrix[5, 3] <- corrMatrix[3, 5] <- -.7
corrMatrix[4, 1] <- corrMatrix[1, 4] <- -.67

corrDF <- expand.grid(row = 1:5, col = 1:5)
corrDF$correlation <- as.vector(corrMatrix)
levelplot(correlation ~ row+ col, corrDF)

findCorrelation(corrMatrix, cutoff = .65, verbose = TRUE)

findCorrelation(corrMatrix, cutoff = .99, verbose = TRUE)