rollshap

Overview

‘rollshap’ is a package that provides analytical computation of rolling and expanding Shapley values for time-series data.

The ‘rollshap’ package decomposes the coefficient of determination (R-squared) of a linear regression into nonnegative contributions from each explanatory variable using the Shapley value from cooperative game theory (Shapley, 1953, doi:10.1515/9781400881970-018). For each window, the exact Shapley value is computed by fitting all subsets of the explanatory variables and averaging the marginal contribution to R-squared across all orderings, which returns an order-invariant attribution that sums to the full-model R-squared. Use cases include:

• Variable importance: rolling decomposition of explanatory power across factors or features
• Factor attribution: quantifying the contribution of each factor to a model’s fit through time
• Feature selection: identifying variables whose marginal contribution is persistent or transient

The package supports rolling and expanding windows, weights, and handling of missing values via ‘min_obs’, ‘complete_obs’, and ‘na_restore’ arguments. The implementation uses the online and offline algorithms from the ‘roll’ package to compute rolling and expanding cross-products efficiently with parallelism across columns and windows provided by ‘RcppParallel’.

Installation

Install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("jasonjfoster/rollshap")

Usage

Load the package and supply a dataset:

library(rollshap) # roll (>= 1.1.7)

n <- 15
m <- 3
x <- matrix(rnorm(n * m), nrow = n, ncol = m)
y <- rnorm(n)
weights <- 0.9 ^ (n:1)

Then, to compute rolling and expanding Shapley values, use the roll_shap function:

# rolling Shapley values with complete windows
roll_shap(x, y, width = 5)

# rolling Shapley values with partial windows
roll_shap(x, y, width = 5, min_obs = 1)

# expanding Shapley values with partial windows
roll_shap(x, y, width = n, min_obs = 1)

# expanding Shapley values with partial windows and weights
roll_shap(x, y, width = n, min_obs = 1, weights = weights)

Note that handling of missing values is supported as well (see the min_obs, complete_obs, and na_restore arguments).

References

Shapley, L.S. (1953). “A Value for n-Person Games.” In Contributions to the Theory of Games, Volume II, edited by H.W. Kuhn and A.W. Tucker, 307-317. Princeton University Press.