Explain Predictions with Global SHAP (Kernel SHAP)

Explain Predictions with Global SHAP (Kernel SHAP)

Usage

explain_kernel(
  model,
  X_explain,
  X_background,
  nsim = 20,
  only_best = FALSE,
  verbose = FALSE
)

Arguments

model

A fitted SuperSurv object OR a single wrapper output.

X_explain

The dataset you want to explain (e.g., X_test[1:10, ]).

X_background

The reference dataset for fastshap (e.g., X_train[1:100, ]).

nsim

Number of simulations. Defaults to 20.

only_best

Logical. If TRUE and model is SuperSurv, only explains the highest-weighted base learner.

verbose

Logical; if TRUE, progress messages are shown.

Value

A data.frame of class c("explain", "data.frame") containing the calculated SHAP values. The columns correspond to the covariates in X_explain.

Examples

if (requireNamespace("fastshap", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  shap_values <- explain_kernel(
    model = fit,
    X_explain = X[1:10, , drop = FALSE],
    X_background = X[11:40, , drop = FALSE],
    nsim = 5
  )

  dim(shap_values)
}
#> [1] 10  5