View source: R/findCorrelation.R

findCorrelation | R Documentation |

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

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

`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 ( |

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

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".

There are several function in the subselect package
(`leaps`

,
`genetic`

,
`anneal`

) that can also be used to accomplish
the same goal but tend to retain more predictors.

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.

Original R code by Dong Li, modified by Max Kuhn

`leaps`

,
`genetic`

,
`anneal`

, `findLinearCombos`

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)

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