Creates parallel coordinates plots for multivariate data visualization. Parallel coordinates plots are excellent for exploring relationships between multiple continuous variables and identifying patterns, clusters, and outliers in high-dimensional data.
Details
This implementation provides comprehensive parallel coordinates functionality:
Multiple scaling methods for variables with different ranges
Grouping support for categorical visualization
Interactive plotly integration for exploration
Summary statistics for variable understanding
Missing data handling options
Super classes
jmvcore::Analysis
-> ClinicoPath::parallelplotBase
-> parallelplotClass
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$translate()
ClinicoPath::parallelplotBase$initialize()