Estimate Brier score VIM

Usage

vim_rsquared(
  time,
  event,
  approx_times,
  landmark_times,
  f_hat,
  fs_hat,
  S_hat,
  G_hat,
  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)
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

Value

data frame giving results