Bootstrap Confidence Intervals for Relative Weights Analysis

Why Bootstrap for RWA?

The Statistical Challenge

As noted by Tonidandel et al. (2009):

“The difficulty in determining the statistical significance of relative weights stems from the fact that the exact (or small sample) sampling distribution of relative weights is unknown.”

Traditional RWA provides point estimates of relative importance but lacks a framework for statistical inference. Bootstrap methods solve this by empirically estimating the sampling distribution of relative weights.

Bootstrap Solution

Bootstrap resampling: 1. Creates multiple samples from your original data 2. Calculates RWA for each bootstrap sample
3. Estimates confidence intervals from the distribution of bootstrap results 4. Enables significance testing by examining whether CIs include zero

Basic Bootstrap Analysis

Simple Bootstrap Example

# Bootstrap analysis with 1000 samples
result_bootstrap <- mtcars %>%
  rwa(outcome = "mpg",
      predictors = c("cyl", "disp", "hp", "gear"),
      bootstrap = TRUE,
      n_bootstrap = 1000,
      conf_level = 0.95)

# View results with confidence intervals
result_bootstrap$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign Raw.RelWeight.CI.Lower
#> 1        hp     0.2321744           29.79691    -             0.17918452
#> 2       cyl     0.2284797           29.32274    -             0.17787979
#> 3      disp     0.2221469           28.50999    -             0.16236804
#> 4      gear     0.0963886           12.37037    +             0.04436607
#>   Raw.RelWeight.CI.Upper Raw.Significant
#> 1              0.2837136            TRUE
#> 2              0.2828943            TRUE
#> 3              0.2876162            TRUE
#> 4              0.1810951            TRUE

Understanding Bootstrap Output

The bootstrap analysis enhances the standard RWA output with:

Raw.RelWeight.CI.Lower/Upper: 95% confidence intervals for raw weights
Raw.Significant: Automatic significance flagging (CI doesn’t include zero)

# Bootstrap-specific information
cat("Bootstrap samples used:", result_bootstrap$bootstrap$n_bootstrap, "\n")
#> Bootstrap samples used: 1000

# Detailed CI information
print(result_bootstrap$bootstrap$ci_results$raw_weights)
#> # A tibble: 4 × 6
#>   variable weight_index ci_lower ci_upper ci_method ci_type
#>   <chr>           <int>    <dbl>    <dbl> <chr>     <chr>  
#> 1 cyl                 1   0.178     0.283 bca       raw    
#> 2 disp                2   0.162     0.288 bca       raw    
#> 3 hp                  3   0.179     0.284 bca       raw    
#> 4 gear                4   0.0444    0.181 bca       raw

# Identify significant predictors
significant_vars <- result_bootstrap$result %>%
  filter(Raw.Significant == TRUE) %>%
  pull(Variables)

cat("Significant predictors:", paste(significant_vars, collapse = ", "))
#> Significant predictors: hp, cyl, disp, gear

Advanced Bootstrap Features

Comprehensive Bootstrap Analysis

For detailed analysis including focal variable comparisons:

# Comprehensive bootstrap with focal variable comparison
result_comprehensive <- mtcars %>%
  rwa(outcome = "mpg",
      predictors = c("cyl", "disp", "hp", "gear", "wt"),
      bootstrap = TRUE,
      comprehensive = TRUE,
      focal = "wt",  # Compare other variables to weight
      n_bootstrap = 500)  # Fewer samples for speed

# Access all bootstrap results
names(result_comprehensive$bootstrap$ci_results)
#> [1] "raw_weights"       "random_comparison" "focal_comparison"

Bootstrap Parameters

Key parameters for bootstrap analysis:

n_bootstrap: Number of bootstrap samples (default: 1000)
conf_level: Confidence level (default: 0.95)
focal: Focal variable for comparative analysis
comprehensive: Enable additional bootstrap tests

# Example with different parameters
custom_bootstrap <- mtcars %>%
  rwa(outcome = "mpg",
      predictors = c("cyl", "disp"),
      bootstrap = TRUE,
      n_bootstrap = 2000,  # More samples for precision
      conf_level = 0.99)   # 99% confidence intervals

custom_bootstrap$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign Raw.RelWeight.CI.Lower
#> 1       cyl     0.3837012           50.51586    -              0.2523797
#> 2      disp     0.3758646           49.48414    -              0.2526669
#>   Raw.RelWeight.CI.Upper Raw.Significant
#> 1              0.4608781            TRUE
#> 2              0.4612490            TRUE

Rescaled Weight Confidence Intervals

Important Considerations

Rescaled weight confidence intervals should be interpreted with caution due to compositional data constraints. They are not recommended for formal statistical inference.

# Rescaled CIs (use with caution)
result_rescaled_ci <- mtcars %>%
  rwa(outcome = "mpg",
      predictors = c("cyl", "disp", "hp"),
      bootstrap = TRUE,
      include_rescaled_ci = TRUE,
      n_bootstrap = 500)

# Note the warning message about interpretation
result_rescaled_ci$result
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign Raw.RelWeight.CI.Lower
#> 1      disp     0.2793550           36.37966    -              0.2068173
#> 2       cyl     0.2723144           35.46279    -              0.2115953
#> 3        hp     0.2162184           28.15755    -              0.1387584
#>   Raw.RelWeight.CI.Upper Raw.Significant Rescaled.RelWeight.CI.Lower
#> 1              0.3400585            TRUE                    30.27135
#> 2              0.3278647            TRUE                    30.37038
#> 3              0.2631454            TRUE                    21.05975
#>   Rescaled.RelWeight.CI.Upper
#> 1                    43.62828
#> 2                    42.92702
#> 3                    37.39395

Why Rescaled CIs Are Problematic

Rescaled weights are compositional data (they sum to 100%), which creates dependencies between variables. This violates assumptions needed for independent confidence intervals.

Recommendation: Focus on raw weight confidence intervals for statistical inference.

Real-World Applications

Diamond Price Analysis

# Analyze diamond price drivers
diamonds_subset <- diamonds %>%
  select(price, carat, depth, table, x, y, z) %>%
  sample_n(1000)  # Sample for faster computation

diamond_rwa <- diamonds_subset %>%
  rwa(outcome = "price",
      predictors = c("carat", "depth", "table", "x", "y", "z"),
      bootstrap = TRUE,
      applysigns = TRUE,
      n_bootstrap = 500)

print(diamond_rwa$result)
#>   Variables Raw.RelWeight Rescaled.RelWeight Sign Sign.Rescaled.RelWeight
#> 1     carat   0.248201410          28.691293    +               28.691293
#> 2         y   0.203994958          23.581168    +               23.581168
#> 3         x   0.202886155          23.452994    +               23.452994
#> 4         z   0.202115201          23.363874    +               23.363874
#> 5     table   0.005545853           0.641083    +                0.641083
#> 6     depth   0.002332141           0.269588    -               -0.269588
#>   Raw.RelWeight.CI.Lower Raw.RelWeight.CI.Upper Raw.Significant
#> 1           0.2324033452            0.264579409            TRUE
#> 2           0.1977835375            0.209208036            TRUE
#> 3           0.1967203566            0.207977647            TRUE
#> 4           0.1955910853            0.207116344            TRUE
#> 5           0.0008624156            0.008449391            TRUE
#> 6          -0.0006655409            0.003155567           FALSE

Interpreting Results

# Focus on significant predictors (results are already sorted by importance)
significant_drivers <- diamond_rwa$result %>%
  filter(Raw.Significant == TRUE) %>%
  select(Variables, Rescaled.RelWeight, Sign.Rescaled.RelWeight)

cat("Significant diamond price drivers (sorted by importance):\n")
#> Significant diamond price drivers (sorted by importance):
print(significant_drivers)
#>   Variables Rescaled.RelWeight Sign.Rescaled.RelWeight
#> 1     carat          28.691293               28.691293
#> 2         y          23.581168               23.581168
#> 3         x          23.452994               23.452994
#> 4         z          23.363874               23.363874
#> 5     table           0.641083                0.641083

cat("\nModel R-squared:", round(diamond_rwa$rsquare, 3))
#> 
#> Model R-squared: 0.865

Best Practices

1. Sample Size Guidelines

# Check your sample size
n_obs <- mtcars %>% 
  select(mpg, cyl, disp, hp, gear) %>% 
  na.omit() %>% 
  nrow()

cat("Sample size:", n_obs)
#> Sample size: 32
cat("\nRecommended bootstrap samples:", min(2000, n_obs * 10))
#> 
#> Recommended bootstrap samples: 320

# Rule of thumb: At least 1000 bootstrap samples, more for smaller datasets

2. Confidence Interval Interpretation

# Examine CI characteristics
ci_data <- result_bootstrap$bootstrap$ci_results$raw_weights
print(head(ci_data))
#> # A tibble: 4 × 6
#>   variable weight_index ci_lower ci_upper ci_method ci_type
#>   <chr>           <int>    <dbl>    <dbl> <chr>     <chr>  
#> 1 cyl                 1   0.178     0.283 bca       raw    
#> 2 disp                2   0.162     0.288 bca       raw    
#> 3 hp                  3   0.179     0.284 bca       raw    
#> 4 gear                4   0.0444    0.181 bca       raw

# Assess precision
ci_analysis <- ci_data %>%
  mutate(
    significant = ci_lower > 0 | ci_upper < 0,
    ci_width = ci_upper - ci_lower,
    precision = case_when(
      ci_width < 0.05 ~ "High precision",
      ci_width < 0.15 ~ "Medium precision", 
      TRUE ~ "Low precision"
    )
  )

print(ci_analysis)
#> # A tibble: 4 × 9
#>   variable weight_index ci_lower ci_upper ci_method ci_type significant ci_width
#>   <chr>           <int>    <dbl>    <dbl> <chr>     <chr>   <lgl>          <dbl>
#> 1 cyl                 1   0.178     0.283 bca       raw     TRUE           0.105
#> 2 disp                2   0.162     0.288 bca       raw     TRUE           0.125
#> 3 hp                  3   0.179     0.284 bca       raw     TRUE           0.105
#> 4 gear                4   0.0444    0.181 bca       raw     TRUE           0.137
#> # ℹ 1 more variable: precision <chr>

3. Bootstrap Method Selection

The package automatically selects the best available bootstrap CI method:

BCA (Bias-Corrected and Accelerated) - Preferred when possible
Percentile - Fallback if BCA fails
Basic bootstrap - Final fallback option

# Check which methods were used
ci_methods <- result_bootstrap$bootstrap$ci_results$raw_weights %>%
  count(ci_method)

print(ci_methods)
#> # A tibble: 1 × 2
#>   ci_method     n
#>   <chr>     <int>
#> 1 bca           4

Performance Considerations

Bootstrap Speed Tips

# For large datasets or many predictors, consider:

# 1. Reduce bootstrap samples for initial exploration
quick_result <- mtcars %>%
  rwa(outcome = "mpg", 
      predictors = c("cyl", "disp"), 
      bootstrap = TRUE, 
      n_bootstrap = 500)  # Faster

# 2. Use comprehensive analysis only when needed
# comprehensive = TRUE adds computational overhead

# 3. Consider parallel processing for very large analyses
# (not currently implemented but could be future enhancement)

Memory Usage

# Bootstrap objects can be large - access specific components
str(result_bootstrap$bootstrap, max.level = 1)
#> List of 6
#>  $ boot_object  :List of 11
#>   ..- attr(*, "class")= chr "boot"
#>   ..- attr(*, "boot_type")= chr "boot"
#>  $ ci_results   :List of 1
#>  $ n_bootstrap  : num 1000
#>  $ conf_level   : num 0.95
#>  $ comprehensive: logi FALSE
#>  $ focal        : NULL

# For memory efficiency, extract only needed results
ci_summary <- result_bootstrap$bootstrap$ci_results$raw_weights %>%
  select(variable, ci_lower, ci_upper, ci_method)

print(ci_summary)
#> # A tibble: 4 × 4
#>   variable ci_lower ci_upper ci_method
#>   <chr>       <dbl>    <dbl> <chr>    
#> 1 cyl        0.178     0.283 bca      
#> 2 disp       0.162     0.288 bca      
#> 3 hp         0.179     0.284 bca      
#> 4 gear       0.0444    0.181 bca

Troubleshooting

Common Bootstrap Issues

# 1. Check for perfect multicollinearity
cor_check <- mtcars %>%
  select(cyl, disp, hp, gear) %>%
  cor()

# Look for correlations = 1.0 (excluding diagonal)
perfect_cor <- which(abs(cor_check) == 1 & cor_check != diag(diag(cor_check)), arr.ind = TRUE)

if(length(perfect_cor) > 0) {
  cat("Perfect multicollinearity detected - remove redundant variables")
} else {
  cat("No perfect multicollinearity detected")
}
#> No perfect multicollinearity detected

# 2. Ensure adequate sample size
min_sample_size <- 5 * length(c("cyl", "disp", "hp", "gear"))  # 5 obs per predictor
actual_sample_size <- nrow(na.omit(mtcars[c("mpg", "cyl", "disp", "hp", "gear")]))

cat("\nMinimum recommended sample size:", min_sample_size)
#> 
#> Minimum recommended sample size: 20
cat("\nActual sample size:", actual_sample_size)
#> 
#> Actual sample size: 32

Reporting Bootstrap Results

Standard Reporting Format

When reporting bootstrap RWA results, include:

Sample size and missing data handling
Bootstrap parameters (number of samples, confidence level)
CI method used (BCA, percentile, basic)
Significant predictors with confidence intervals
Model fit (R-squared)

Example Report

# Generate a summary report
report_data <- result_bootstrap$result %>%
  filter(Raw.Significant == TRUE) %>%
  arrange(desc(Rescaled.RelWeight)) %>%
  select(Variables, Rescaled.RelWeight, Raw.RelWeight.CI.Lower, Raw.RelWeight.CI.Upper)

cat("Relative Weights Analysis Results\n")
#> Relative Weights Analysis Results
cat("=================================\n")
#> =================================
cat("Sample size:", result_bootstrap$n, "\n")
#> Sample size: 32
cat("Bootstrap samples:", result_bootstrap$bootstrap$n_bootstrap, "\n")
#> Bootstrap samples: 1000
cat("Model R-squared:", round(result_bootstrap$rsquare, 3), "\n\n")
#> Model R-squared: 0.779
cat("Significant Predictors:\n")
#> Significant Predictors:
print(report_data)
#>   Variables Rescaled.RelWeight Raw.RelWeight.CI.Lower Raw.RelWeight.CI.Upper
#> 1        hp           29.79691             0.17918452              0.2837136
#> 2       cyl           29.32274             0.17787979              0.2828943
#> 3      disp           28.50999             0.16236804              0.2876162
#> 4      gear           12.37037             0.04436607              0.1810951

References

Bootstrap Methods in RWA:

Tonidandel, S., LeBreton, J. M., & Johnson, J. W. (2009). Determining the statistical significance of relative weights. Psychological Methods, 14(4), 387-399.

General Bootstrap Theory:

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall/CRC.

Compositional Data Analysis:

Aitchison, J. (1986). The statistical analysis of compositional data. Chapman & Hall.

Conclusion

Bootstrap confidence intervals provide a robust solution for statistical inference in Relative Weights Analysis. By following the guidelines in this vignette, researchers can:

Determine statistical significance of predictor importance
Report confidence intervals with appropriate interpretations
Avoid common pitfalls in bootstrap analysis
Apply best practices for reliable results

The bootstrap functionality in the rwa package represents a significant advancement in making RWA a complete tool for both exploratory analysis and confirmatory research.