Comprehensive Cox proportional hazards model diagnostic plots using ggcoxdiagnostics from the survminer package. This module provides essential model validation tools for survival analysis, including residual analysis, proportional hazards assumption testing, and influence diagnostics.
Details
This module implements the diagnostic capabilities requested in GitHub Issue #61, providing comprehensive model validation for Cox regression analysis. The diagnostic plots help identify model violations, influential observations, and assess overall model fit quality essential for clinical research applications.
Key diagnostic plot types:
Martingale residuals: Detect non-linear relationships and outliers
Deviance residuals: Identify poorly fitted observations
Score residuals: Assess influential observations
Schoenfeld residuals: Test proportional hazards assumption
DFBeta plots: Evaluate influence of individual observations
Super classes
jmvcore::Analysis -> ClinicoPath::coxdiagnosticsBase -> coxdiagnosticsClass
Methods
Inherited methods
jmvcore::Analysis$.createImage()jmvcore::Analysis$.createImages()jmvcore::Analysis$.createPlotObject()jmvcore::Analysis$.load()jmvcore::Analysis$.render()jmvcore::Analysis$.save()jmvcore::Analysis$.savePart()jmvcore::Analysis$.setCheckpoint()jmvcore::Analysis$.setParent()jmvcore::Analysis$.setReadDatasetHeaderSource()jmvcore::Analysis$.setReadDatasetSource()jmvcore::Analysis$.setResourcesPathSource()jmvcore::Analysis$.setStatePathSource()jmvcore::Analysis$addAddon()jmvcore::Analysis$asProtoBuf()jmvcore::Analysis$asSource()jmvcore::Analysis$check()jmvcore::Analysis$init()jmvcore::Analysis$optionsChangedHandler()jmvcore::Analysis$postInit()jmvcore::Analysis$print()jmvcore::Analysis$readDataset()jmvcore::Analysis$run()jmvcore::Analysis$serialize()jmvcore::Analysis$setError()jmvcore::Analysis$setStatus()jmvcore::Analysis$translate()ClinicoPath::coxdiagnosticsBase$initialize()