importance: Extract predictor variable importance from a fusion model

In ummel/fusionModel: Data fusion and analysis of synthetic data in R

Extract predictor variable importance from a fusion model

Description

Returns predictor variable (feature) importance of underlying LightGBM models stored in a fusion model file (.fsn) on disk.

Usage

importance(fsn)

Arguments

fsn	Character. Path to fusion model file (.fsn) generated by train.

Details

Importance metrics are computed via lgb.importance. Three types of measures are returned; "gain" is typically the preferred measure.

Value

A named list containing detailed and summary importance results. The summary results are most useful, as they return the average importance of each predictor across potentially multiple underlying LightGBM models; i.e. zero ("z"), mean ("m"), or quantile ("q") models. See Examples for suggested plotting of results.

Examples

# Build a fusion model using RECS microdata
# Note that "fusion_model.fsn" will be written to working directory
?recs
fusion.vars <- c("electricity", "natural_gas", "aircon")
predictor.vars <- names(recs)[2:12]
fsn.path <- train(data = recs, y = fusion.vars, x = predictor.vars)

# Extract predictor variable importance
ximp <- importance(fsn.path)

# Plot summary results
library(ggplot2)
ggplot(ximp$summary, aes(x = x, y = gain)) +
  geom_bar(stat = "identity") +
  facet_grid(~ y) +
  coord_flip()

# View detailed results
View(ximp$detailed)

ummel/fusionModel documentation built on June 1, 2025, 11 p.m.