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Create a Relative Weights Analysis (RWA)

Source: R/rwa_multiregress.R
rwa_multiregress.Rd

This function creates a Relative Weights Analysis (RWA) and returns a list of outputs. RWA provides a heuristic method for estimating the relative weight of predictor variables in multiple regression, which involves creating a multiple regression with on a set of transformed predictors which are orthogonal to each other but maximally related to the original set of predictors. rwa_multiregress() is optimised for dplyr pipes and shows positive / negative signs for weights.

Usage

rwa_multiregress(df, outcome, predictors, applysigns = FALSE)

Arguments

df

Data frame or tibble to be passed through.

outcome

Outcome variable, to be specified as a string or bare input. Must be a numeric variable.

predictors

Predictor variable(s), to be specified as a vector of string(s) or bare input(s). All variables must be numeric.

applysigns

Logical value specifying whether to show an estimate that applies the sign. Defaults to FALSE.

Value

rwa_multiregress() returns a list of outputs, as follows:

  • predictors: character vector of names of the predictor variables used.

  • rsquare: the rsquare value of the regression model.

  • result: the final output of the importance metrics.

    • The Rescaled.RelWeight column sums up to 100.

    • The Sign column indicates whether a predictor is positively or negatively correlated with the outcome.

  • n: indicates the number of observations used in the analysis.

  • lambda: the transformation matrix that maps the original correlated predictors to orthogonal variables while preserving their relationship to the outcome. Used internally to compute relative weights.

  • RXX: Correlation matrix of all the predictor variables against each other.

  • RXY: Correlation values of the predictor variables against the outcome variable.

Details

rwa_multiregress() produces raw relative weight values (epsilons) as well as rescaled weights (scaled as a percentage of predictable variance) for every predictor in the model. Signs are added to the weights when the applysigns argument is set to TRUE. See https://relativeimportance.davidson.edu/multipleregression.html for the original implementation that inspired this package.

Examples

# Basic multiple regression RWA
result <- rwa_multiregress(
  df = mtcars,
  outcome = "mpg",
  predictors = c("cyl", "disp", "hp", "wt")
)

# View the relative importance results
result$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign
#> 1       cyl     0.2096904           24.70914    -
#> 2      disp     0.1883043           22.18908    -
#> 3        hp     0.1799590           21.20570    -
#> 4        wt     0.2706812           31.89607    -

# With sign information
result_signed <- rwa_multiregress(
  df = mtcars,
  outcome = "mpg",
  predictors = c("cyl", "disp", "hp", "wt"),
  applysigns = TRUE
)
result_signed$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign Sign.Rescaled.RelWeight
#> 1       cyl     0.2096904           24.70914    -               -24.70914
#> 2      disp     0.1883043           22.18908    -               -22.18908
#> 3        hp     0.1799590           21.20570    -               -21.20570
#> 4        wt     0.2706812           31.89607    -               -31.89607