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Alternating conditional expectations correlation

Source: R/pair_methods.R
pair_ace.Rd

Calculates the maximal correlation coefficient from alternating conditional expectations algorithm for every variable pair in a dataset.

Usage

pair_ace(d, handle.na = TRUE, warnings = TRUE, ...)

Arguments

d

A dataframe

handle.na

If TRUE uses pairwise complete observations, otherwise NAs not handled.

warnings

If TRUE, generates a warning for datasets of one row, one column, or with constant variables.

...

other arguments

Value

A tibble of class pairwise with a maximal correlation from the alternating conditional expectations algorithm for every variable pair

Details

The maximal correlation is calculated using alternating conditional expectations algorithm which find the transformations of variables such that the squared correlation is maximised. The ace function from acepack package is used for the calculation.

References

Breiman, Leo, and Jerome H. Friedman. "Estimating optimal transformations for multiple regression and correlation." Journal of the American statistical Association 80.391 (1985): 580-598.

Examples

pair_ace(iris)
#> # A tibble: 10 × 6
#>    x            y            score group value pair_type
#>    <chr>        <chr>        <chr> <chr> <dbl> <chr>    
#>  1 Petal.Length Sepal.Length ace   all   0.913 nn       
#>  2 Petal.Width  Sepal.Length ace   all   0.865 nn       
#>  3 Sepal.Length Sepal.Width  ace   all   0.584 nn       
#>  4 Petal.Length Sepal.Width  ace   all   0.706 nn       
#>  5 Petal.Width  Sepal.Width  ace   all   0.731 nn       
#>  6 Petal.Length Petal.Width  ace   all   0.989 nn       
#>  7 Sepal.Length Species      ace   all   0.838 fn       
#>  8 Sepal.Width  Species      ace   all   0.679 fn       
#>  9 Petal.Length Species      ace   all   0.994 fn       
#> 10 Petal.Width  Species      ace   all   0.994 fn