Workflow Patterns

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Workflow Patterns

Why workflow patterns help

Most users do not need every feature in ReproStat at once. They usually need a small number of reliable patterns that fit real analysis situations.

This article shows several practical ways to use the package.

Pattern 1: Standard regression stability check

Use this when you already have a preferred regression specification and want to know how stable its outputs are.

diag_obj <- run_diagnostics(
  mpg ~ wt + hp + disp,
  data = mtcars,
  B = 200,
  method = "bootstrap"
)

reproducibility_index(diag_obj)
#> $index
#> [1] 89.42195
#> 
#> $components
#>       coef     pvalue  selection prediction 
#>  0.9294961  0.8566667  0.8150000  0.9757153
selection_stability(diag_obj)
#>    wt    hp  disp 
#> 1.000 1.000 0.445

Recommended outputs to review:

reproducibility_index()
selection_stability()
plot_stability(diag_obj, "selection")

Pattern 2: Sample-composition sensitivity

Use this when you are worried that the model depends too strongly on exactly which observations appear in the sample.

diag_sub <- run_diagnostics(
  mpg ~ wt + hp + disp,
  data = mtcars,
  B = 200,
  method = "subsample",
  frac = 0.75
)

reproducibility_index(diag_sub)
#> $index
#> [1] 89.61837
#> 
#> $components
#>       coef     pvalue  selection prediction 
#>  0.9665288  0.7833333  0.8450000  0.9898728

This is often useful for:

smaller datasets
observational studies
analyses where influence of sample composition is a concern

Pattern 3: Measurement-noise stress test

Use noise perturbation when predictors may be measured with minor error and you want to know whether the fitted result is sensitive to that noise.

diag_noise <- run_diagnostics(
  mpg ~ wt + hp + disp,
  data = mtcars,
  B = 150,
  method = "noise",
  noise_sd = 0.05
)

reproducibility_index(diag_noise)
#> $index
#> [1] 97.65751
#> 
#> $components
#>       coef     pvalue  selection prediction 
#>  0.9977687  1.0000000  0.9088889  0.9996427
prediction_stability(diag_noise)$mean_variance
#> [1] 0.01297961

This does not replace a full measurement-error model, but it gives a useful practical stress test.

Pattern 4: Logistic model reproducibility

Use a GLM backend when the response is binary.

diag_glm <- run_diagnostics(
  am ~ wt + hp + qsec,
  data = mtcars,
  B = 150,
  backend = "glm",
  family = stats::binomial()
)

reproducibility_index(diag_glm)
#> $index
#> [1] 74.84598
#> 
#> $components
#>       coef     pvalue  selection prediction 
#>  0.2502762  1.0000000  0.8355556  0.9080076

This pattern is helpful when you want to know whether the classification-style conclusion is stable under perturbation, not just whether the original fit looks significant.

Pattern 5: Robust regression with outlier concern

If you suspect outliers or heavy tails are affecting the OLS result, compare a robust backend.

if (requireNamespace("MASS", quietly = TRUE)) {
  diag_rlm <- run_diagnostics(
    mpg ~ wt + hp + disp,
    data = mtcars,
    B = 150,
    backend = "rlm"
  )

  reproducibility_index(diag_rlm)
}
#> Warning in rlm.default(x, y, weights, method = method, wt.method = wt.method, :
#> 'rlm' failed to converge in 20 steps
#> Warning in rlm.default(x, y, weights, method = method, wt.method = wt.method, :
#> 'rlm' failed to converge in 20 steps
#> Warning in rlm.default(x, y, weights, method = method, wt.method = wt.method, :
#> 'rlm' failed to converge in 20 steps
#> Warning in rlm.default(x, y, weights, method = method, wt.method = wt.method, :
#> 'rlm' failed to converge in 20 steps
#> $index
#> [1] 88.48408
#> 
#> $components
#>       coef     pvalue  selection prediction 
#>  0.9253969  0.7733333  0.8644444  0.9761887

This is often a useful companion analysis rather than a full replacement.

Pattern 6: Penalized regression and variable retention

Use glmnet when you care about regularized modeling and whether variables are selected consistently.

if (requireNamespace("glmnet", quietly = TRUE)) {
  diag_lasso <- run_diagnostics(
    mpg ~ wt + hp + disp + qsec,
    data = mtcars,
    B = 150,
    backend = "glmnet",
    en_alpha = 1
  )

  reproducibility_index(diag_lasso)
  selection_stability(diag_lasso)
}
#>        wt        hp      disp      qsec 
#> 1.0000000 0.9533333 0.4400000 0.6800000

Here, selection stability is especially informative because it reflects non-zero retention frequency.

Pattern 7: Compare candidate models by ranking stability

When you have multiple plausible formulas, repeated CV ranking stability helps you see whether one model wins consistently or only occasionally.

models <- list(
  compact  = mpg ~ wt + hp,
  standard = mpg ~ wt + hp + disp,
  expanded = mpg ~ wt + hp + disp + qsec
)

cv_obj <- cv_ranking_stability(models, mtcars, v = 5, R = 40)
cv_obj$summary
#>      model mean_rmse   sd_rmse mean_rank top1_frequency
#> 1  compact  2.684893 0.1629443     1.050           0.95
#> 2 expanded  2.811187 0.1605096     2.425           0.05
#> 3 standard  2.798253 0.1474168     2.525           0.00

Focus on two columns:

mean_rank: lower is better on average
top1_frequency: higher means the model is more often the winner

Those two quantities are related but not identical.

Pattern 8: Reporting a compact reproducibility section

A practical reporting workflow is:

fit a diagnostic object with your primary model
compute the RI and a confidence interval
report the most unstable component
include one or two plots
if model selection is part of the analysis, add CV ranking stability

Example:

diag_obj <- run_diagnostics(
  mpg ~ wt + hp + disp,
  data = mtcars,
  B = 150,
  method = "bootstrap"
)

ri <- reproducibility_index(diag_obj)
ci <- ri_confidence_interval(diag_obj, R = 300, seed = 1)

ri
#> $index
#> [1] 88.21907
#> 
#> $components
#>       coef     pvalue  selection prediction 
#>  0.9166282  0.8000000  0.8400000  0.9721348
ci
#>     2.5%    97.5% 
#> 86.65629 89.91509

A practical decision checklist

Before running a large analysis, decide:

which perturbation method reflects your concern
how many iterations B are feasible
whether prediction stability should be in-sample or on predict_newdata
whether you want a standard, robust, or penalized backend
whether model comparison is part of the task

Next steps

Use these patterns as templates, then adapt them to your own data, formulas, and modeling constraints. For conceptual interpretation, read the interpretation article. For function-level details, use the reference pages.

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.