caret (version 7.0-1)

findCorrelation: Determine highly correlated variables

Description

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

Usage

findCorrelation(
  x,
  cutoff = 0.9,
  verbose = FALSE,
  names = FALSE,
  exact = ncol(x) < 100
)

Value

A vector of indices denoting the columns to remove (when names = TRUE) otherwise a vector of column names. If no correlations meet the criteria, integer(0) is returned.

Arguments

x

A correlation matrix

cutoff

A numeric value for the pair-wise absolute correlation cutoff

verbose

A boolean for printing the details

names

a logical; should the column names be returned (TRUE) or the column index (FALSE)?

exact

a logical; should the average correlations be recomputed at each step? See Details below.

Author

Original R code by Dong Li, modified by Max Kuhn

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.

Using exact = TRUE will cause the function to re-evaluate the average correlations at each step while exact = FALSE uses all the correlations regardless of whether they have been eliminated or not. The exact calculations will remove a smaller number of predictors but can be much slower when the problem dimensions are "big".

See Also

findLinearCombos

Examples

R1 <- structure(c(1, 0.86, 0.56, 0.32, 0.85, 0.86, 1, 0.01, 0.74, 0.32,
                  0.56, 0.01, 1, 0.65, 0.91, 0.32, 0.74, 0.65, 1, 0.36,
                  0.85, 0.32, 0.91, 0.36, 1),
                .Dim = c(5L, 5L))
colnames(R1) <- rownames(R1) <- paste0("x", 1:ncol(R1))
R1

findCorrelation(R1, cutoff = .6, exact = FALSE)
findCorrelation(R1, cutoff = .6, exact = TRUE)
findCorrelation(R1, cutoff = .6, exact = TRUE, names = FALSE)


R2 <- diag(rep(1, 5))
R2[2, 3] <- R2[3, 2] <- .7
R2[5, 3] <- R2[3, 5] <- -.7
R2[4, 1] <- R2[1, 4] <- -.67

corrDF <- expand.grid(row = 1:5, col = 1:5)
corrDF$correlation <- as.vector(R2)
levelplot(correlation ~ row + col, corrDF)

findCorrelation(R2, cutoff = .65, verbose = TRUE)

findCorrelation(R2, cutoff = .99, verbose = TRUE)