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 = 10)

Arguments

data: dataframe that is to be rearranged
target: target correlation matrix. Must have same dimensions as number of columns in data-frame.
passes: Number of optimization passes (default = 10). Increasing this value MAY improve results if n-columns (target correlation matrix dimensions) are many. Decreasing the value for 'passes' is faster but may decrease accuracy.

Value

Returns a dataframe 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 -0.121 0.125
#> x2 -0.121  1.000 0.171
#> x3  0.125  0.171 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 0.50 0.749
#> X2 0.500 1.00 0.250
#> X3 0.749 0.25 1.000