Create a Relative Weights Analysis (RWA)

Source: R/rwa.R

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() is optimised for dplyr pipes and shows positive / negative signs for weights.

rwa(df, outcome, predictors, applysigns = FALSE, plot = TRUE)

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.
plot

Logical value specifying whether to plot the rescaled importance metrics.

Value

rwa() 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:

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

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

Details

rwa() 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

library(ggplot2)
rwa(diamonds,"price",c("depth","carat"))

#> $predictors
#> [1] "depth" "carat"
#> 
#> $rsquare
#> [1] 0.8506755
#> 
#> $result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign
#> 1     depth   0.000729149         0.08571412    -
#> 2     carat   0.849946308        99.91428588    +
#> 
#> $n
#> [1] 53940
#> 
#> $lambda
#>            [,1]       [,2]
#> [1,] 0.99990040 0.01411356
#> [2,] 0.01411356 0.99990040
#> 
#> $RXX
#>            depth      carat
#> depth 1.00000000 0.02822431
#> carat 0.02822431 1.00000000
#> 
#> $RXY
#>      depth      carat 
#> -0.0106474  0.9215913 
#>