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Create a Relative Weights Analysis with logistic regression

Source: R/rwa_logit.R
rwa_logit.Rd

This function performs Relative Weights Analysis (RWA) for binary outcome variables using logistic regression. RWA provides a method for estimating the relative importance of predictor variables by transforming them into orthogonal variables while preserving their relationship to the outcome. This implementation follows Johnson (2000) for logistic regression.

Usage

rwa_logit(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_logit() returns a list of outputs, as follows:

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

  • rsquare: the pseudo R-squared value (sum of epsilon weights) for the logistic 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 associated with the outcome.

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

  • lambda: the Lambda transformation matrix from the analysis.

Examples

# Create a binary outcome variable
mtcars_binary <- mtcars
mtcars_binary$high_mpg <- ifelse(mtcars$mpg > median(mtcars$mpg), 1, 0)

# Basic logistic RWA
result <- rwa_logit(
  df = mtcars_binary,
  outcome = "high_mpg",
  predictors = c("cyl", "disp", "hp", "wt")
)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

# View the relative importance results
result$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign
#> 1       cyl      7.296847           22.22380    -
#> 2      disp      7.343709           22.36653    -
#> 3        hp      4.937049           15.03663    -
#> 4        wt     13.255876           40.37304    -

# With sign information
result_signed <- rwa_logit(
  df = mtcars_binary,
  outcome = "high_mpg",
  predictors = c("cyl", "disp", "hp", "wt"),
  applysigns = TRUE
)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
result_signed$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign      Sign
#> 1       cyl      7.296847           22.22380    - -22.22380
#> 2      disp      7.343709           22.36653    - -22.36653
#> 3        hp      4.937049           15.03663    - -15.03663
#> 4        wt     13.255876           40.37304    - -40.37304