Package 'rollshap'

Rolling Shapley Values

Description

Analytical computation of rolling Shapley values for time-series data.

Details

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

Author(s)

Jason Foster

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Rolling Shapley Values

Description

A function for computing the rolling and expanding Shapley values of time-series data.

Usage

roll_shap(x, y, width, weights = rep(1, width), intercept = TRUE,
  min_obs = width, complete_obs = TRUE, na_restore = FALSE,
  online = TRUE)

Arguments

x	vector or matrix. Rows are observations and columns are the independent variables.
y	vector or matrix. Rows are observations and columns are the dependent variables.
width	integer. Window size.
weights	vector. Weights for each observation within a window.
intercept	logical. Either TRUE to include or FALSE to remove the intercept.
min_obs	integer. Minimum number of observations required to have a value within a window, otherwise result is NA.
complete_obs	logical. If TRUE then rows containing any missing values are removed, if FALSE then pairwise is used.
na_restore	logical. Should missing values be restored?
online	logical. Process observations using an online algorithm.

Value

An object of the same class and dimension as x with the rolling and expanding Shapley values.

Examples

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

# 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)

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