findCorrelation(x, cutoff = 0.9, verbose = FALSE, names = FALSE, exact = ncol(x) < 100)
TRUE
) or
the column index (FALSE
)?names
= TRUE
) otherwise a vector of column names. If no correlations meet the
criteria, integer(0)
is returned.
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
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)