caret (version 6.0-79)

# 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)```

## 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.

## 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.

## 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".

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.

`leaps`, `genetic`, `anneal`, `findLinearCombos`

## Examples

Run this code
```# NOT RUN {
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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