Statistical Layer Actions

These are direct immutable ChartProgram actions. Each accepts one option object and returns a new program.

createIntervalData

createIntervalData({
  id, source?, field, groupBy?, center?, extent?, level?, as?
})

Create immutable grouped center/lower/upper summary rows. Mean supports standard error, sample standard deviation, and Student-t confidence intervals; median supports interquartile range. Data

createRegression

createRegression({
  target?, x?, y?, groupBy?, method?, degree?, span?,
  confidence?, interval?, band?, line?
})

Infer an eligible point layer and create immutable fitted data, optional grouped interval-band paths, and grouped line paths. Method defaults to "linear"; polynomial degree to 2; LOESS span to 0.75. Regression

editRegression

editRegression({
  target?, data?, x?, y?, groupBy?, method?, degree?, span?, confidence?,
  interval?, band?, line?
})

Revise the model through its stable point owner. Data-role or statistical changes create and rebind one immutable derived-data revision; groupBy: false removes grouping. Component-only changes retain the current fitted rows. Regression

createErrorBar

createErrorBar({
  id?, target?, data?, x?, y?, xOffset?, yOffset?, groupBy?, coordinate?,
  caps?, capSize?, stroke?, strokeWidth?, strokeDash?, opacity?
} = {})

Create vertical or horizontal statistical or explicit intervals. With one eligible encoded layer, the shortest call infers its fields, orientation, data, coordinate, and scales. Explicit interval fields also allow the independent position to be quantitative. A categorical source can also infer a matching xOffset/yOffset; its field joins statistical grouping and aligns source points, the main rule, and both caps on one shared sub-slot scale. Error bars

editErrorBar

editErrorBar({
  target?, caps?, capSize?, stroke?, strokeWidth?, strokeDash?, opacity?,
  statistics?
})

Partially edit one error bar and its owned caps. statistics revises a statistical interval through immutable data; explicit interval owners reject that option. caps: false removes both caps and caps: true restores them. Error bars

createErrorBand

createErrorBand({
  id?, target?, data?, x?, y?, groupBy?, coordinate?, fill?, opacity?,
  curve?, boundaries?
} = {})

Create a vertical or horizontal statistical or explicit interval ribbon. The action can infer one encoded source layer and reuses createIntervalData, an ordinary area, the matching atomic range action, and grouping actions. boundaries: { stroke?, strokeWidth?, strokeDash?, opacity?, curve? } adds lower and upper line layers. Boundary curve inherits the area curve unless it is overridden. Error bands

editErrorBand and editErrorBandBoundary

editErrorBand({ target?, fill?, opacity?, curve?, statistics?, boundaries? })
editErrorBandBoundary({
  target?, boundary?, stroke?, strokeWidth?, strokeDash?, opacity?, curve?
})

Edit the band body, statistical interval, or both owned boundary components without addressing generated line IDs. boundaries: false disables both; an object creates or edits both. The focused boundary action still accepts "both", "lower", or "upper" and creates missing selected boundaries. Error bands

createBoxPlot

createBoxPlot({
  id?, target?, data?, x?, y?, coordinate?, whisker?, width?, outliers?,
  box?, median?, outlier?, guides?
} = {})

Create a vertical or horizontal Tukey/min–max box plot from one categorical and one quantitative field. The action infers an encoded source when possible and composes immutable box summary data, error-bar whiskers, ranged-bar bodies, median rules, and optional point outliers. Tukey factor, band width, component appearance, and outlier creation are configurable. Box plots Guides remain opt-in for compatibility: pass guides: {} or nested options to create them inside the facade; omission and false create none.

editBoxPlot

editBoxPlot({ target?, whisker?, width?, outliers?, box?, median?, outlier? })

Revise box statistics, optional outlier topology, width, and component appearance through the stable box owner without addressing generated child IDs. Box plots

createGradientPlot

createGradientPlot({
  id?, target?, data?, x?, y?, coordinate?, density?, width?, gradient?,
  center?, guides?
} = {})

Create one density-gradient strip per category from categorical and quantitative x/y roles. Positions can be explicit, inferred from one eligible encoded layer, or completed later. Defaults are Gaussian auto density, 64 samples, width band 0.7, no outline, a median center rule, and applicable guides. A categorical encodeColor owns strip hue while density continues to control lightness and opacity. Statistical actions

editGradientPlot

editGradientPlot({ target?, density?, width?, gradient?, center? })

Revise one stable gradient-plot owner. Statistical changes create and rebind one immutable raw-source profile revision; appearance-only edits retain it. center: false removes the optional rule and center: {} restores it. Statistical actions

createViolinPlot

createViolinPlot({
  id?, data?, coordinate?, x, y, split?, color?, density?, area?, guides?
})

Create a vertical or horizontal categorical density plot from exactly one categorical and one quantitative x/y role. The action infers field types, orientation, data, scales, and applicable guides, then records an ordinary area mark, categorical encodeDensity, optional color, and guides as wrapped children. Density options own bandwidth, extent, kernel, normalization, and shared or independent band-relative width. An optional two-value split assigns one half to each side of the category center. Violin plots

Related

Action Reference · Chart API · Supported Features