vim_brier function

Estimate Brier score VIM

Estimate Brier score VIM

vim_brier( time, event, approx_times, landmark_times, f_hat, fs_hat, S_hat, G_hat, cf_folds, ss_folds, sample_split, scale_est = FALSE, alpha = 0.05 )

Arguments

  • time: n x 1 numeric vector of observed follow-up times If there is censoring, these are the minimum of the event and censoring times.
  • event: n x 1 numeric vector of status indicators of whether an event was observed. Defaults to a vector of 1s, i.e. no censoring.
  • approx_times: Numeric vector of length J1 giving times at which to approximate integrals.
  • landmark_times: Numeric vector of length J2 giving times at which to estimate Brier score
  • f_hat: Full oracle predictions (n x J1 matrix)
  • fs_hat: Residual oracle predictions (n x J1 matrix)
  • S_hat: Estimates of conditional event time survival function (n x J2 matrix)
  • G_hat: Estimate of conditional censoring time survival function (n x J2 matrix)
  • cf_folds: Numeric vector of length n giving cross-fitting folds
  • ss_folds: Numeric vector of length n giving sample-splitting folds
  • sample_split: Logical indicating whether or not to sample split
  • scale_est: Logical, whether or not to force the VIM estimate to be nonnegative
  • alpha: The level at which to compute confidence intervals and hypothesis tests. Defaults to 0.05

Returns

A data frame giving results, with the following columns: - landmark_time: Time at which AUC is evaluated.

  • est: VIM point estimate.

  • var_est: Estimated variance of the VIM estimate.

  • cil: Lower bound of the VIM confidence interval.

  • ciu: Upper bound of the VIM confidence interval.

  • cil_1sided: Lower bound of a one-sided confidence interval.

  • p: p-value corresponding to a hypothesis test of null importance.

  • large_predictiveness: Estimated predictiveness of the large oracle prediction function.

  • small_predictiveness: Estimated predictiveness of the small oracle prediction function.

  • vim: VIM type.

  • large_feature_vector: Group of features available for the large oracle prediction function.

  • small_feature_vector: Group of features available for the small oracle prediction function.

See Also

vim for example usage