Enhanced Partial Correlation Analysis with BlueSky Integration

Enhanced partial correlation analysis toolkit with BlueSky integration for comprehensive correlation analysis while controlling for confounding variables. Includes partial and semi-partial correlations, robust error handling, multiple variable support, and advanced statistical inference. Perfect for clinicopathological research where controlling for covariates is essential.

Usage

partialcorrelation(
  data,
  vars,
  controls,
  method = "pearson",
  ci = TRUE,
  ciWidth = 95,
  sig = TRUE,
  sigLevel = 0.05,
  matrixPlot = FALSE,
  showZeroOrder = TRUE,
  correlationType = "partial",
  multipleComparison = "none",
  robustErrorHandling = TRUE,
  showDiagnostics = FALSE,
  detailedOutput = FALSE,
  bootstrapCI = FALSE,
  bootstrapSamples = 1000,
  showEffectSizes = TRUE,
  assumptionChecks = FALSE,
  clinicalInterpretation = TRUE,
  showRecommendations = FALSE,
  bluesky_integration = TRUE,
  comprehensive_output = FALSE
)

Arguments

data

.

vars

.

controls

.

method

.

ci

.

ciWidth

.

sig

.

sigLevel

.

matrixPlot

.

showZeroOrder

.

correlationType

Type of correlation analysis (BlueSky BSkyPartialSemiCorrelations feature)

multipleComparison

Method for correcting p-values for multiple comparisons

robustErrorHandling

Use BlueSky-style error handling with graceful degradation

showDiagnostics

Display diagnostic information about computations and assumptions

detailedOutput

Include comprehensive statistical details and interpretations

bootstrapCI

Calculate bootstrap confidence intervals for correlations

bootstrapSamples

Number of bootstrap samples for confidence intervals

showEffectSizes

Include Cohen's interpretation of correlation effect sizes

assumptionChecks

Perform and report assumption checks for correlation analysis

clinicalInterpretation

Provide clinical context and interpretation guidance

showRecommendations

Provide recommendations based on analysis results

bluesky_integration

Use BlueSky R statistical environment features

comprehensive_output

Include comprehensive statistical details and diagnostics

Value

A results object containing:

results$instructions					a html
results$partialCorr					a table
results$zeroOrder					a table
results$semipartialCorr					a table
results$diagnostics					Computational diagnostics and assumption checks
results$assumptionResults					Results of statistical assumption testing
results$comprehensiveAnalysisSummary					Enhanced statistical summary with BlueSky integration
results$recommendations					a html
results$clinicalInterpretationGuide					a html
results$methodsExplanation					a html
results$plot					an image
results$partialCorrelationNetwork					an image
results$effectSizePlot					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$partialCorr$asDF

as.data.frame(results$partialCorr)