rwa {rwa} | R Documentation |
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
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 |
sort |
Logical value specifying whether to sort results by rescaled relative weights in descending order. Defaults to |
bootstrap |
Logical value specifying whether to calculate bootstrap confidence intervals. Defaults to |
n_bootstrap |
Number of bootstrap samples to use when bootstrap = TRUE. Defaults to 1000. |
conf_level |
Confidence level for bootstrap intervals. Defaults to 0.95. |
focal |
Focal variable for bootstrap comparisons (optional). |
comprehensive |
Whether to run comprehensive bootstrap analysis including random variable and focal comparisons. |
include_rescaled_ci |
Logical value specifying whether to include confidence intervals for rescaled weights. Defaults to |
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