Comparison with Alternatives

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Gilles Colling

2025-11-28

Overview

This vignette compares corrselect’s graph-theoretic approach to four established methods for multicollinearity management and variable selection:

caret::findCorrelation() - Greedy correlation-based pruning
Boruta - Random forest permutation importance
glmnet - L1/L2 regularization (LASSO/Ridge)
Manual VIF removal - Iterative variance inflation factor thresholding

Each comparison examines algorithmic differences, performance characteristics, and appropriate use cases. Evaluations use the bioclim_example dataset (19 bioclimatic variables, \(n = 100\)).

See vignette("theory") for mathematical foundations. See vignette("quickstart") for usage examples.

Evaluation Dataset

data(bioclim_example)
predictors <- bioclim_example[, -1]  # Exclude response
response <- bioclim_example[, 1]

cat("Variables:", ncol(predictors), "\n")
#> Variables: 19
cat("Observations:", nrow(predictors), "\n")
#> Observations: 100
cat("Response: species_richness (continuous)\n")
#> Response: species_richness (continuous)

cor_matrix <- cor(predictors)

# Correlation heatmap
col_pal <- colorRampPalette(c("#3B4992", "white", "#EE0000"))(100)

par(mar = c(1, 1, 3, 1))
nc <- ncol(cor_matrix)
nr <- nrow(cor_matrix)
image(seq_len(nc), seq_len(nr), t(cor_matrix[nr:1, ]),
      col = col_pal,
      xlab = "", ylab = "", axes = FALSE,
      main = "Bioclimatic Variable Correlations (p = 19)",
      zlim = c(-1, 1))
axis(1, at = seq_len(nc), labels = colnames(cor_matrix), las = 2, cex.axis = 0.7)
axis(2, at = nc:1, labels = colnames(cor_matrix), las = 2, cex.axis = 0.7)

for (i in seq_len(nc)) {
  for (j in seq_len(nr)) {
    text_col <- if (abs(cor_matrix[j, i]) > 0.6) "white" else "black"
    text(i, nr - j + 1, sprintf("%.2f", cor_matrix[j, i]),
         cex = 0.5, col = text_col)
  }
}

Block structure present: correlations range from -0.15 to 0.97.

Comparison 1: caret::findCorrelation()

Method

caret’s findCorrelation() applies greedy iterative removal:

Identify pair with maximum \(|r_{ij}|\)
Remove variable with larger mean absolute correlation
Repeat until all \(|r_{ij}| < \tau\)

Non-deterministic: results depend on internal ordering. Typically removes more variables than graph-theoretic methods.

Execution

if (requireNamespace("caret", quietly = TRUE)) {
  # Apply caret's greedy algorithm
  to_remove_caret <- caret::findCorrelation(cor_matrix, cutoff = 0.7)
  result_caret <- predictors[, -to_remove_caret]

  cat("caret results:\n")
  cat("  Variables retained:", ncol(result_caret), "\n")
  cat("  Variables removed:", length(to_remove_caret), "\n")
  cat("  Removed:", paste(colnames(predictors)[to_remove_caret], collapse = ", "), "\n")
}
#> caret results:
#>   Variables retained: 10 
#>   Variables removed: 9 
#>   Removed: BIO8, BIO7, BIO5, BIO4, BIO3, BIO9, BIO1, BIO11, BIO15

# Apply corrselect (exact mode)
result_corrselect <- corrPrune(predictors, threshold = 0.7, mode = "exact")

cat("\ncorrselect results:\n")
#> 
#> corrselect results:
cat("  Variables retained:", ncol(result_corrselect), "\n")
#>   Variables retained: 12
cat("  Variables removed:", length(attr(result_corrselect, "removed_vars")), "\n")
#>   Variables removed: 7
cat("  Removed:", paste(attr(result_corrselect, "removed_vars"), collapse = ", "), "\n")
#>   Removed: BIO2, BIO4, BIO5, BIO7, BIO8, BIO10, BIO15

corrselect retains more variables (\(|S_{\text{corrselect}}| \ge |S_{\text{caret}}|\)) via maximal clique enumeration while satisfying identical threshold constraint.

Distribution Comparison

# Extract correlations
cor_orig <- cor(predictors)
cor_corrselect <- cor(result_corrselect)

if (requireNamespace("caret", quietly = TRUE)) {
  cor_caret <- cor(result_caret)

  # Overlaid histogram comparing all three
  hist(abs(cor_orig[upper.tri(cor_orig)]),
       breaks = 30,
       main = "Distribution of Absolute Correlations",
       xlab = "Absolute Correlation",
       col = rgb(0.5, 0.5, 0.5, 0.4),
       xlim = c(0, 1))

  hist(abs(cor_caret[upper.tri(cor_caret)]),
       breaks = 30,
       col = rgb(0.8, 0.2, 0.2, 0.4),
       add = TRUE)

  hist(abs(cor_corrselect[upper.tri(cor_corrselect)]),
       breaks = 30,
       col = rgb(0.2, 0.5, 0.8, 0.4),
       add = TRUE)

  abline(v = 0.7, col = "black", lwd = 2, lty = 2)

  legend("topright",
         legend = c(
           paste0("Original (", ncol(predictors), " vars)"),
           paste0("caret (", ncol(result_caret), " vars)"),
           paste0("corrselect (", ncol(result_corrselect), " vars)"),
           "Threshold"
         ),
         fill   = c(
           rgb(0.5, 0.5, 0.5, 0.4),
           rgb(0.8, 0.2, 0.2, 0.4),
           rgb(0.2, 0.5, 0.8, 0.4),
           NA
         ),
         border = c("white", "white", "white", NA),
         lty    = c(NA, NA, NA, 2),
         lwd    = c(NA, NA, NA, 2),
         col    = c(NA, NA, NA, "black"),
         bty    = "o",
         bg = "white")
}

Comparison

Feature	caret	corrselect
Algorithm	Greedy iterative removal	Maximal clique enumeration
Optimality	Heuristic	Exact (mode = “exact”)
Reproducibility	Non-deterministic	Deterministic
Variables retained	\(\le\) optimal	Maximal
Forced variables	No	Yes (`force_in`)
Mixed data	No	Yes (`assocSelect`)
Complexity	\(O(p^2)\)	\(O(p^2)\) greedy, \(O(3^{p/3})\) exact

Applications

caret: Exploratory analysis, non-critical reproducibility requirements.

corrselect: Reproducible research, maximal variable retention, forced variable constraints, mixed data types.

Comparison 2: Boruta

Method

Boruta tests variable importance via random forest permutation:

Create shadow features (permuted copies)
Fit random forest on original + shadow features
Test: \(\text{importance}(X_i) > \max(\text{importance}(\text{shadow}))\)
Iteratively confirm/reject until convergence

Orthogonal objective: Boruta selects predictive variables (supervised). corrselect removes redundant variables (unsupervised).

Execution

if (requireNamespace("Boruta", quietly = TRUE)) {
  # Boruta: "Which variables predict species_richness?"
  set.seed(123)
  boruta_result <- Boruta::Boruta(
    species_richness ~ .,
    data    = bioclim_example,
    maxRuns = 100
  )

  cat("Boruta variable importance screening:\n")
  print(table(boruta_result$finalDecision))

  important_vars <- names(boruta_result$finalDecision[
    boruta_result$finalDecision == "Confirmed"
  ])

  cat("\n  Confirmed predictors:", length(important_vars), "\n")
  cat(" ", paste(important_vars, collapse = ", "), "\n")
}
#> Boruta variable importance screening:
#> 
#> Tentative Confirmed  Rejected 
#>         0         6        13 
#> 
#>   Confirmed predictors: 6 
#>   BIO12, BIO13, BIO14, BIO15, BIO16, BIO17

# corrselect: "Which variables are redundant?"
corrselect_result <- corrPrune(predictors, threshold = 0.7)

cat("\ncorrselect multicollinearity pruning:\n")
#> 
#> corrselect multicollinearity pruning:
cat("  Non-redundant variables:", ncol(corrselect_result), "\n")
#>   Non-redundant variables: 12
cat(" ", paste(names(corrselect_result), collapse = ", "), "\n")
#>   BIO1, BIO3, BIO6, BIO9, BIO11, BIO12, BIO13, BIO14, BIO16, BIO17, BIO18, BIO19

Different variable sets selected: Boruta optimizes prediction; corrselect minimizes redundancy.

Comparison

Criterion	Boruta	corrselect
Objective	Predictive power	Redundancy removal
Criterion	Permutation importance	\(\\|r_{ij}\\| < \tau\)
Response	Required	Not required
Multicollinearity	Indirect	Direct
Stochastic	Yes	No
Complexity	High (\(\ge 100\) forests)	Low (graph)

Sequential Application

# Stage 1: Correlation-based pruning
data_pruned <- corrPrune(raw_data, threshold = 0.7)

# Stage 2: Importance testing
boruta_result <- Boruta::Boruta(response ~ ., data = cbind(response, data_pruned))
final_vars <- names(boruta_result$finalDecision[
  boruta_result$finalDecision == "Confirmed"
])

# Stage 3: Final model
final_model <- lm(response ~ ., data = cbind(response, data_pruned)[, c("response", final_vars)])

Stage 1 removes redundancy (reproducible). Stage 2 tests importance (stochastic). Stage 3 fits model with non-redundant, predictive variables.

Applications

Boruta: Prediction-focused analysis with response variable.

corrselect: Multicollinearity removal, exploratory analysis without response, reproducible selection.

Sequential: High-dimensional correlated data requiring both redundancy removal and importance testing.

Comparison 3: glmnet (LASSO/Ridge)

Method

glmnet minimizes regularized loss:

\[ \min_{\beta} \frac{1}{2n} \|y - X\beta\|_2^2 + \lambda \left[\alpha \|\beta\|_1 + (1-\alpha) \|\beta\|_2^2\right] \]

\(\alpha = 1\): LASSO (L1 penalty, sparse \(\beta\))
\(\alpha = 0\): Ridge (L2 penalty, shrinkage)
\(\lambda\): Cross-validation selected

Difference: glmnet performs soft selection (shrinkage) optimizing prediction. corrselect performs hard selection (removal) based on correlation structure.

Execution

if (requireNamespace("glmnet", quietly = TRUE)) {
  # Fit LASSO with cross-validation
  X <- as.matrix(predictors)
  y <- response

  set.seed(123)
  cv_lasso <- glmnet::cv.glmnet(X, y, alpha = 1)

  # Extract non-zero coefficients at lambda.1se (conservative choice)
  coef_lasso <- stats::coef(cv_lasso, s = "lambda.1se")
  selected_lasso <- rownames(coef_lasso)[coef_lasso[, 1] != 0][-1]  # Remove intercept

  cat("glmnet (LASSO, λ = lambda.1se):\n")
  cat("  Variables retained:", length(selected_lasso), "\n")
  cat(" ", paste(selected_lasso, collapse = ", "), "\n")
}
#> glmnet (LASSO, λ = lambda.1se):
#>   Variables retained: 4 
#>   BIO1, BIO12, BIO15, BIO16

if (requireNamespace("glmnet", quietly = TRUE)) {
  # Compare model performance
  model_glmnet <- lm(species_richness ~ .,
                     data = bioclim_example[, c("species_richness", selected_lasso)])

  model_corrselect <- lm(species_richness ~ .,
                         data = cbind(species_richness = response, result_corrselect))

  cat("\nModel comparison (OLS on selected variables):\n")
  cat("  glmnet:     R² =", round(summary(model_glmnet)$r.squared, 3),
      "with", length(selected_lasso), "predictors\n")
  cat("  corrselect: R² =", round(summary(model_corrselect)$r.squared, 3),
      "with", ncol(result_corrselect), "predictors\n")
}
#> 
#> Model comparison (OLS on selected variables):
#>   glmnet:     R² = 0.983 with 4 predictors
#>   corrselect: R² = 0.909 with 12 predictors

glmnet selects fewer variables (\(|S_{\text{glmnet}}| \le |S_{\text{corrselect}}|\)) optimizing prediction. corrselect maximizes retention under correlation constraint.

Coefficient Comparison

if (requireNamespace("glmnet", quietly = TRUE)) {
  par(mfrow = c(1, 2), mar = c(8, 4, 3, 2))

  # glmnet coefficients (shrinkage)
  coef_vals <- coef_lasso[coef_lasso[, 1] != 0, ][-1]
  barplot(sort(abs(coef_vals), decreasing = TRUE),
          las = 2,
          main = "glmnet: Shrunk Coefficients",
          ylab = "Absolute Coefficient Value",
          col = "salmon",
          cex.names = 0.7)

  # corrselect: unbiased OLS coefficients
  coef_corrselect <- coef(model_corrselect)[-1]  # Remove intercept
  barplot(sort(abs(coef_corrselect), decreasing = TRUE),
          las = 2,
          main = "corrselect: Unbiased OLS Coefficients",
          ylab = "Absolute Coefficient Value",
          col = rgb(0.2, 0.5, 0.8, 0.7),
          cex.names = 0.7)
}

Left: L1 penalty shrinks coefficients toward zero (biased). Right: OLS on pruned variables (unbiased). glmnet optimizes prediction with shrinkage. corrselect preserves effect sizes.

Comparison

Criterion	glmnet	corrselect
Objective	Prediction accuracy	Multicollinearity removal
Selection	Soft (shrinkage)	Hard (removal)
Coefficient bias	Yes (L1/L2)	No
Multicollinearity	Regularization	Pruning
Response	Required	Not required
Tuning	\(\lambda\) (cross-validation)	\(\tau\) (user-specified)
Interpretability	Shrunk effects	Direct effects

Applications

glmnet: Prediction-focused, high-dimensional (\(p > n\)), accepts biased coefficients.

corrselect: Interpretable coefficients, exploratory analysis, explicit correlation constraint, unregularized modeling.

Sequential: Correlation pruning (corrselect) followed by sparse prediction (glmnet).

Comparison 4: modelPrune() vs Manual VIF Removal

Method

Variance Inflation Factor quantifies predictor multicollinearity:

\[ \text{VIF}_j = \frac{1}{1 - R^2_j} \]

where \(R^2_j\) results from regressing \(X_j\) on remaining predictors. Thresholds:

VIF < 5: Low collinearity
VIF < 10: Moderate (acceptable)
VIF ≥ 10: High (problematic)

Manual approach: iteratively remove max(VIF) until all VIF < threshold.

Manual Implementation

# Manual iterative VIF removal
manual_vif_removal <- function(formula, data, threshold = 5, max_iter = 10) {
  require(car)

  # Get response variable name
  response_var <- all.vars(formula)[1]

  # Get predictor names (handles ~ . notation)
  model <- lm(formula, data = data)
  current_vars <- names(coef(model))[-1]  # Exclude intercept

  removed_vars <- character(0)
  vif_vals <- car::vif(model)

  while (max(vif_vals) > threshold && length(current_vars) > 1 && length(removed_vars) < max_iter) {
    # Remove variable with highest VIF
    var_to_remove <- names(which.max(vif_vals))
    removed_vars <- c(removed_vars, var_to_remove)
    cat("Iteration", length(removed_vars), ": Removing", var_to_remove,
        "(VIF =", round(max(vif_vals), 2), ")\n")

    # Update variable list and refit
    current_vars <- setdiff(current_vars, var_to_remove)
    new_formula <- as.formula(paste(response_var, "~", paste(current_vars, collapse = " + ")))
    model <- lm(new_formula, data = data)
    vif_vals <- car::vif(model)
  }

  list(model = model, iterations = length(removed_vars),
       vif = vif_vals, removed = removed_vars, converged = max(vif_vals) <= threshold)
}

# Run manual VIF removal
if (requireNamespace("car", quietly = TRUE)) {
  cat("Manual VIF removal (iterative):\n")
  manual_result <- manual_vif_removal(species_richness ~ ., data = bioclim_example, threshold = 5)
  cat("\nVariables kept:", length(manual_result$vif), "\n")
  if (!manual_result$converged) {
    cat("(Stopped at max_iter = 10; VIF threshold not yet reached)\n")
  }
}
#> Manual VIF removal (iterative):
#> Loading required package: car
#> Loading required package: carData
#> Iteration 1 : Removing BIO2 (VIF = 5.83 )
#> Iteration 2 : Removing BIO7 (VIF = 5.66 )
#> Iteration 3 : Removing BIO5 (VIF = 5.03 )
#> 
#> Variables kept: 16

modelPrune() Comparison

# Run modelPrune
modelprune_result <- modelPrune(species_richness ~ ., data = bioclim_example, limit = 5)

cat("\nmodelPrune results:\n")
#> 
#> modelPrune results:
cat("Variables removed:", attr(modelprune_result, "removed_vars"), "\n")
#> Variables removed: BIO2 BIO7 BIO5
cat("Variables kept:", length(attr(modelprune_result, "selected_vars")), "\n")
#> Variables kept: 16

# Extract final model
final_model <- attr(modelprune_result, "final_model")
if (requireNamespace("car", quietly = TRUE)) {
  cat("\nFinal VIF values:\n")
  print(round(car::vif(final_model), 2))
}
#> 
#> Final VIF values:
#>  BIO1  BIO3  BIO4  BIO6  BIO8  BIO9 BIO10 BIO11 BIO12 BIO13 BIO14 BIO15 BIO16 
#>  2.09  3.68  3.81  2.57  4.03  4.27  4.96  3.06  1.76  2.51  3.11  3.01  2.43 
#> BIO17 BIO18 BIO19 
#>  2.88  2.82  1.70

Visual: VIF Comparison

if (requireNamespace("car", quietly = TRUE)) {
  # Compute VIF for original model
  model_full <- lm(species_richness ~ ., data = bioclim_example)
  vif_before <- car::vif(model_full)

  # VIF after modelPrune
  vif_after <- car::vif(final_model)

  # Combined barplot
  par(mar = c(8, 4, 4, 2))
  all_vars <- unique(c(names(vif_before), names(vif_after)))
  vif_combined <- data.frame(
    before = vif_before[match(all_vars, names(vif_before))],
    after = vif_after[match(all_vars, names(vif_after))]
  )
  vif_combined[is.na(vif_combined)] <- 0
  vif_combined <- vif_combined[order(vif_combined$before, decreasing = TRUE), ]

  # Show top 15
  n_show <- min(15, nrow(vif_combined))
  barplot(t(as.matrix(vif_combined[1:n_show, ])),
          beside = TRUE,
          las = 2,
          main = "VIF Before and After modelPrune()",
          ylab = "VIF",
          col = c(rgb(0.8, 0.2, 0.2, 0.7), rgb(0.2, 0.5, 0.8, 0.7)),
          cex.names = 0.6,
          names.arg = rownames(vif_combined)[1:n_show])
  abline(h = 5, col = "black", lwd = 2, lty = 2)
  legend("topright",
         legend = c("Before", "After", "Limit = 5"),
         fill   = c(rgb(0.8, 0.2, 0.2, 0.7), rgb(0.2, 0.5, 0.8, 0.7), NA),
         border = c("white", "white", NA),
         lty    = c(NA, NA, 2),
         lwd    = c(NA, NA, 2),
         col    = c(NA, NA, "black"),
         bty    = "o",
         bg = "white")
}

Comparison

Criterion	Manual VIF	modelPrune()
Algorithm	Iterative greedy	Graph-based
Automation	Manual	Automated
Optimality	Heuristic	Maximal subset
Forced variables	Manual exclusion	`force_in`
Output	Verbose log	Summary

Applications

Manual VIF: Educational use, diagnostic understanding, legacy workflows.

modelPrune(): Production pipelines, forced variable constraints, reproducible documentation.

Summary

Method Selection

Goal	Primary Method	Alternative
Redundancy removal (unsupervised)	corrPrune()	caret::findCorrelation()
VIF reduction (regression)	modelPrune()	Manual VIF
Predictive variable selection	Boruta	RF importance
Prediction accuracy	glmnet	Elastic net
Mixed data types	assocSelect()	Manual metrics
Forced variable constraints	corrselect (`force_in`)	N/A
Exploratory (fast)	caret	corrPrune (greedy)

corrselect Distinguishing Features

Maximal clique enumeration: Optimal retention under \(|r_{ij}| < \tau\) constraint
Deterministic: ELS and Bron-Kerbosch algorithms guarantee reproducibility
Flexible: Forced variables, mixed types, greedy/exact modes
Unbiased estimates: Hard removal preserves coefficient interpretability
Model-agnostic: Correlation-based preprocessing

Integrated Workflow

# Correlation pruning
data_pruned <- corrPrune(raw_data, threshold = 0.7)

# VIF refinement
model_data <- modelPrune(response ~ ., data = data_pruned, limit = 5)

# Importance testing (optional)
if (requireNamespace("Boruta", quietly = TRUE)) {
  boruta_result <- Boruta::Boruta(response ~ ., data = model_data)
  important_vars <- names(boruta_result$finalDecision[
    boruta_result$finalDecision == "Confirmed"
  ])
}

# Final model: OLS (interpretable) or glmnet (prediction)
final_model <- lm(response ~ ., data = model_data[, c("response", important_vars)])

References

caret: Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1-26. doi:10.18637/jss.v028.i05
Boruta: Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. Journal of Statistical Software, 36(11), 1-13. doi:10.18637/jss.v036.i11
glmnet: Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. doi:10.18637/jss.v033.i01
VIF: Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley. doi:10.1002/0471725153

Session Info

sessionInfo()
#> R version 4.5.1 (2025-06-13 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#> 
#> Matrix products: default
#>   LAPACK version 3.12.1
#> 
#> locale:
#> [1] LC_COLLATE=C                          
#> [2] LC_CTYPE=English_United States.utf8   
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.utf8    
#> 
#> time zone: Europe/Luxembourg
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] car_3.1-3            carData_3.0-5        microbenchmark_1.5.0
#> [4] corrselect_3.0.2    
#> 
#> loaded via a namespace (and not attached):
#>  [1] shape_1.4.6.1        gtable_0.3.6         xfun_0.53           
#>  [4] bslib_0.9.0          ggplot2_4.0.0        recipes_1.3.1       
#>  [7] lattice_0.22-7       vctrs_0.6.5          tools_4.5.1         
#> [10] generics_0.1.4       stats4_4.5.1         parallel_4.5.1      
#> [13] tibble_3.3.0         pkgconfig_2.0.3      ModelMetrics_1.2.2.2
#> [16] Matrix_1.7-4         data.table_1.17.8    RColorBrewer_1.1-3  
#> [19] S7_0.2.0             lifecycle_1.0.4      compiler_4.5.1      
#> [22] farver_2.1.2         stringr_1.5.2        textshaping_1.0.3   
#> [25] codetools_0.2-20     Boruta_9.0.0         htmltools_0.5.8.1   
#> [28] class_7.3-23         sass_0.4.10          glmnet_4.1-10       
#> [31] yaml_2.3.10          Formula_1.2-5        prodlim_2025.04.28  
#> [34] pillar_1.11.1        jquerylib_0.1.4      MASS_7.3-65         
#> [37] cachem_1.1.0         gower_1.0.2          iterators_1.0.14    
#> [40] abind_1.4-8          rpart_4.1.24         foreach_1.5.2       
#> [43] nlme_3.1-168         parallelly_1.45.1    lava_1.8.2          
#> [46] tidyselect_1.2.1     digest_0.6.37        stringi_1.8.7       
#> [49] future_1.67.0        dplyr_1.1.4          reshape2_1.4.4      
#> [52] purrr_1.2.0          listenv_0.9.1        splines_4.5.1       
#> [55] fastmap_1.2.0        grid_4.5.1           cli_3.6.5           
#> [58] magrittr_2.0.4       survival_3.8-3       future.apply_1.20.0 
#> [61] withr_3.0.2          scales_1.4.0         lubridate_1.9.4     
#> [64] timechange_0.3.0     rmarkdown_2.30       globals_0.18.0      
#> [67] nnet_7.3-20          timeDate_4051.111    ranger_0.17.0       
#> [70] evaluate_1.0.5       knitr_1.50           hardhat_1.4.2       
#> [73] caret_7.0-1          rlang_1.1.6          Rcpp_1.1.0          
#> [76] glue_1.8.0           pROC_1.19.0.1        ipred_0.9-15        
#> [79] svglite_2.2.2        rstudioapi_0.17.1    jsonlite_2.0.0      
#> [82] R6_2.6.1             plyr_1.8.9           systemfonts_1.3.1

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

Comparison with Alternatives

Gilles Colling

2025-11-28

Overview

Evaluation Dataset

Comparison 1: caret::findCorrelation()

Method

Execution

Distribution Comparison

Comparison

Applications

Comparison 2: Boruta

Method

Execution

Comparison

Sequential Application

Applications

Comparison 3: glmnet (LASSO/Ridge)

Method

Execution

Coefficient Comparison

Comparison

Applications

Comparison 4: modelPrune() vs Manual VIF Removal

Method

Manual Implementation

modelPrune() Comparison

Visual: VIF Comparison

Comparison

Applications

Summary

Method Selection

corrselect Distinguishing Features

Integrated Workflow

References

See Also

Session Info