Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Extract confidence intervals from bootstrap object

Description

Extract confidence intervals from bootstrap object

Usage

extract_ci(
  boot_object,
  conf_level = 0.95,
  variable_names = NULL,
  ci_type = "raw"
)

Arguments

Value

Data frame with confidence intervals

Plot the rescaled importance values from the output of rwa()

Description

Pass the output of rwa() and plot a bar chart of the rescaled importance values. Signs are always calculated and taken into account, which is equivalent to setting the applysigns argument to TRUE in rwa().

Usage

plot_rwa(rwa)

Arguments

Examples

library(ggplot2)
# Use a smaller sample for faster execution
diamonds_small <- diamonds[sample(nrow(diamonds), 1000), ]
diamonds_small %>%
  rwa(outcome = "price",
      predictors = c("depth","carat", "x", "y", "z"),
      applysigns = TRUE) %>%
  plot_rwa()

Remove any columns where all the values are missing

Description

Pass a data frame and returns a version where all columns made up of entirely missing values are removed.

Usage

remove_all_na_cols(df)

Arguments

Details

This is used within rwa().

Run bootstrap analysis for RWA

Description

Run bootstrap analysis for RWA

Usage

run_rwa_bootstrap(
  data,
  outcome,
  predictors,
  n_bootstrap = 1000,
  conf_level = 0.95,
  focal = NULL,
  comprehensive = FALSE,
  include_rescaled = FALSE
)

Arguments

Value

List with bootstrap results and confidence intervals

Create a Relative Weights Analysis (RWA)

Description

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.

Usage

rwa(
  df,
  outcome,
  predictors,
  applysigns = FALSE,
  sort = TRUE,
  bootstrap = FALSE,
  n_bootstrap = 1000,
  conf_level = 0.95,
  focal = NULL,
  comprehensive = FALSE,
  include_rescaled_ci = FALSE
)

Arguments

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://www.scotttonidandel.com/rwa-web for the original implementation that inspired this package.

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 (sorted by Rescaled.RelWeight in descending order by default).

  • The Rescaled.RelWeight column sums up to 100.

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

  • When bootstrap = TRUE, includes confidence interval columns for raw weights.

  • Rescaled weight CIs are available via include_rescaled_ci = TRUE but not recommended for inference.

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

• bootstrap: bootstrap results (only present when bootstrap = TRUE), containing:

  • ci_results: confidence intervals for weights

  • boot_object: raw bootstrap object for advanced analysis

  • n_bootstrap: number of bootstrap samples used

• lambda:

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

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

Examples

library(ggplot2)
# Basic RWA (results sorted by default)
rwa(diamonds,"price",c("depth","carat"))

# RWA without sorting (preserves original predictor order)
rwa(diamonds,"price",c("depth","carat"), sort = FALSE)


# For faster examples, use a subset of data for bootstrap
diamonds_small <- diamonds[sample(nrow(diamonds), 1000), ]

# RWA with bootstrap confidence intervals (raw weights only)
rwa(diamonds_small,"price",c("depth","carat"), bootstrap = TRUE, n_bootstrap = 100)

# Include rescaled weight CIs (use with caution for inference)
rwa(diamonds_small,"price",c("depth","carat"), bootstrap = TRUE, 
    include_rescaled_ci = TRUE, n_bootstrap = 100)

# Comprehensive bootstrap analysis with focal variable
result <- rwa(diamonds_small,"price",c("depth","carat","table"), 
              bootstrap = TRUE, comprehensive = TRUE, focal = "carat", 
              n_bootstrap = 100)
# View confidence intervals
result$bootstrap$ci_results