parcats requires an alluvial plot created with easyalluvial to create an interactive parrallel categories diagram.


Parcats from alluvial from data in wide format

p = alluvial_wide(mtcars2, max_variables = 5)

parcats(p, marginal_histograms = TRUE, data_input = mtcars2)

Parcats from model response alluvial

Machine Learning models operate in a multidimensional space and their response is hard to visualise. Model response and partial dependency plots attempt to visualise ML models in a two dimensional space. Using alluvial plots or parrallel categories diagrams we can increase the number of dimensions.

Here we see the response of a random forest model if we vary the three variables with the highest importance while keeping all other features at their median/mode value.

df = select(mtcars2, -ids )
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp, degree = 3)
pred = predict(m, newdata = dspace)
p = alluvial_model_response(pred, dspace, imp, degree = 3)

parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)