Intelligent recommendations for heteroscedasticity diagnostics

These helpers assemble an end-to-end recommendation workflow that profiles the input dataset, selects appropriate heteroscedasticity diagnostics, interprets results with qualitative confidence labels, and suggests remediation strategies with decision-tree style guidance.

Usage

analyseDatasetCharacteristics(data, response = NULL)

suggestDiagnosticsForProfile(profile)

interpretHeteroTestResults(test_results, alpha = 0.05)

recommendRemediationStrategies(test_results, model, data, profile = NULL)

warnDiagnosticAssumptions(profile, interpretations, model, data, test_results)

buildDiagnosticDecisionTree(profile, recommendations)

composeDiagnosticNarrative(profile, interpretations, remediation,
  assumption_warnings, decision_tree)

generateHeteroRecommendations(model, data = NULL, test_results = NULL,
  alpha = 0.05, include_report = TRUE)

Arguments

data

A data.frame containing the modelling variables.

response

Optional response variable name used for labelling.

profile

Dataset profile produced by analyseDatasetCharacteristics().

test_results

Named list of htest objects, typically returned by runHeteroTests().

alpha

Significance level applied when interpreting p-values.

model

Fitted lm/glm object or model formula.

interpretations

Output from interpretHeteroTestResults().

recommendations

Output from suggestDiagnosticsForProfile(), used to build the decision tree.

remediation

Output from recommendRemediationStrategies().

assumption_warnings

Character vector of generated warnings.

decision_tree

Data frame returned by buildDiagnosticDecisionTree().

include_report

Logical; include a plain-language narrative in the output.

Value

analyseDatasetCharacteristics() returns a list describing dataset size, missingness, skewness, and risk factors. suggestDiagnosticsForProfile() returns a data frame of recommended diagnostics with rationales. interpretHeteroTestResults() yields a tidy data frame of interpretations and an overall summary. recommendRemediationStrategies() produces a structured remediation plan. warnDiagnosticAssumptions() returns warnings about assumption violations. buildDiagnosticDecisionTree() constructs a tidy decision tree. composeDiagnosticNarrative() outputs a plain-language character vector, and generateHeteroRecommendations() returns an object of class hetero_recommendation_report bundling all components.

Details

The recommendation engine analyses dataset characteristics such as sample size, missingness, and predictor skewness to propose diagnostics tailored to the context. It automatically interprets test outcomes, attaches qualitative confidence labels, warns when assumptions are at risk, and suggests remediation strategies matched to detected variance patterns. The generated decision tree and narrative target non-specialist audiences who need actionable guidance.

See also

runHeteroTests(), suggestRemediation(), generateDiagnosticReport()

Examples

model <- lm(mpg ~ wt + hp, data = mtcars)
recs <- generateHeteroRecommendations(model, mtcars)
print(recs)
#> Heteroscedasticity Recommendation Overview
#> --------------------------------------------------
#> Observations: 32 | Variables: 11
#> Sample size category: small
#> Missingness: none (0.0%)
#> 
#> Key recommendations:
#> - breusch_pagan: Baseline regression-based heteroscedasticity check.
#> - quantile_regression: Variables carb show strong skewness; quantile slope comparisons provide a complementary distributional check.
#> - rank_permutation: Rank-based test is robust when asymptotics are unreliable.
#> - white: Detects general forms of non-constant variance.
#> - wild_bootstrap: Bootstrap inference performs well in small samples.
#> 
#> Summary:
#> No strong evidence of heteroscedasticity across the evaluated diagnostics. 
#> No remediation required. 
#> 
#> Narrative excerpt:
#> Heteroscedasticity Diagnostic Recommendation Report
#> Observations: 32; Variables: 11
#> Sample size category: small
#> Missingness level: none (0.0% overall)
#> Response variable: mpg
#>  
#> ...