Rearrange elements in each column of a data-frame to fit a predefined correlation matrix

lcor() rearranges values in each column of a data-frame so that columns are correlated to match a predefined correlation matrix.

Usage

lcor(data, target, passes = 10L)

Arguments

data

data-frame that is to be rearranged

target

target correlation matrix. Must have same dimensions as number of columns in data-frame.

passes

number of passes when searching for suitable permutation. Default = 10. You may get a better result if dealing with a large number of columns.

Value

Returns a data frame whose column-wise correlations approximate a user-specified correlation matrix

Details

Values in a column do not change, so univariate statistics remain the same.

Examples

## parameters
n <- 32
lowerbound <- 1
upperbound <- 5
items <- 5

mydat3 <- data.frame(
  x1 = lfast(n, 2.5, 0.75, lowerbound, upperbound, items),
  x2 = lfast(n, 3.0, 1.50, lowerbound, upperbound, items),
  x3 = lfast(n, 3.5, 1.00, lowerbound, upperbound, items)
)
#> reached maximum of 1024 iterations
#> reached maximum of 1024 iterations
#> reached maximum of 1024 iterations

cor(mydat3) |> round(3)
#>        x1     x2     x3
#> x1  1.000  0.006 -0.122
#> x2  0.006  1.000 -0.164
#> x3 -0.122 -0.164  1.000

tgt3 <- matrix(
  c(
    1.00, 0.50, 0.75,
    0.50, 1.00, 0.25,
    0.75, 0.25, 1.00
  ),
  nrow = 3, ncol = 3
)

## apply function
new3 <- lcor(mydat3, tgt3)

## test output
cor(new3) |> round(3)
#>       X1    X2    X3
#> X1 1.000 0.500 0.749
#> X2 0.500 1.000 0.248
#> X3 0.749 0.248 1.000