Introduction to SAPP

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Mohamed Amine FARES

2026-07-28

Overview

SAPP (Sector-Adjusted Points Plot) is an R package that visualizes feature dominance in a two-dimensional space. It combines PCA for dimensionality reduction with a novel sector-adjustment mechanism to show which features drive predictions for which observations.

Quick Start

Load the package and prepare your data:

library(sappviz)
data(iris)
X <- iris[, 1:4]

Fit a linear model and compute importances:

model <- lm(Petal.Width ~ Sepal.Length + Sepal.Width + Petal.Length, data = iris)
imp <- abs(coef(model)[-1])
names(imp) <- c("Sepal.Length", "Sepal.Width", "Petal.Length")

Compute per-observation influence and plot:

inf <- influence_feature(X, model)
plot_sapp(X, imp, inf, alpha = "auto")
#> Auto-Alpha selected: alpha = 0.54
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the sappviz package.
#>   Please report the issue at <https://github.com/FaresAminu/sappviz/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: The following aesthetics were dropped during statistical transformation: size.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?

Interpretation

Colors: The feature that most influenced each observation.
Point size: How strongly that feature dominated.
Black markers: Sector centers, based on global importances.
Contours: Where each feature tends to dominate.

Advanced Usage: Random Forest

SAPP supports tree-based models via SHAP values (requires fastshap):

library(randomForest)
rf <- randomForest(Species ~ ., data = iris, importance = TRUE)
imp_rf <- importance(rf)[, "MeanDecreaseGini"]
names(imp_rf) <- colnames(X)
inf_rf <- influence_feature(X, rf)
plot_sapp(X, imp_rf, inf_rf, alpha = "auto")

References

Lundberg, S. M., and Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. NIPS.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.