viviBartPlot function

viviBartPlot

Plots a Heatmap showing variable importance on the diagonal and variable interaction on the off-diagonal with uncertainty included.

viviBartPlot(
  matrix,
  intPal = NULL,
  impPal = NULL,
  intLims = NULL,
  impLims = NULL,
  uncIntLims = NULL,
  uncImpLims = NULL,
  unc_levels = 4,
  max_desat = 0.6,
  pow_desat = 0.2,
  max_light = 0.6,
  pow_light = 1,
  angle = 0,
  border = FALSE,
  label = NULL
)

Arguments

• matrix: Matrices, such as that returned by viviBartMatrix, of values to be plotted.
• intPal: A vector of colours to show interactions, for use with scale_fill_gradientn. Palette number has to be 2^x/2
• impPal: A vector of colours to show importance, for use with scale_fill_gradientn. Palette number has to be 2^x/2
• intLims: Specifies the fit range for the color map for interaction strength.
• impLims: Specifies the fit range for the color map for importance.
• uncIntLims: Specifies the fit range for the color map for interaction strength uncertainties.
• uncImpLims: Specifies the fit range for the color map for importance uncertainties.
• unc_levels: The number of uncertainty levels
• max_desat: The maximum desaturation level.
• pow_desat: The power of desaturation level.
• max_light: The maximum light level.
• pow_light: The power of light level.
• angle: The angle to rotate the x-axis labels. Defaults to zero.
• border: Logical. If TRUE then draw a black border around the diagonal elements.
• label: legend label for the uncertainty measure.

Returns

Either a heatmap, VSUP, or quantile heatmap plot.

Examples

if(requireNamespace("dbarts", quietly = TRUE)){
 # Load the dbarts package to access the bart function
 library(dbarts)
 # Get Data
 df <- na.omit(airquality)
 # Create Simple dbarts Model For Regression:
 set.seed(1701)
 dbartModel <- bart(df[2:6], df[, 1], ntree = 5, keeptrees = TRUE, nskip = 10, ndpost = 10)

 # Tree Data
 trees_data <- extractTreeData(model = dbartModel, data = df)

 # VSUP Matrix
 vsupMat <- viviBartMatrix(trees = trees_data,
                           type = 'vsup',
                           metric = 'propMean',
                           metricError = 'CV')
 # Plot
 viviBartPlot(vsupMat, label = 'CV')
 }