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Bootstrap uncertainty for the neural rem() backend

Source: R/rem_nn_uncertainty.R
nn_uncertainty.Rd

Quantifies uncertainty for a rem(method = "nn") fit by a stratum bootstrap: the case-control strata are resampled with replacement, the network is refit on each resample (reusing the original nn_control() settings, including the training engine), and the spread across refits yields partial-dependence uncertainty bands and a concordance confidence interval. This is the inferential counterpart that the point-prediction nn backend otherwise lacks (coef() returns NULL).

Usage

nn_uncertainty(
  object,
  data,
  B = 200L,
  case = NULL,
  stratum = NULL,
  n_grid = 50L,
  level = 0.95,
  seed = NULL
)

Arguments

object

A fitted rem() object with method = "nn".

data

The case-control data frame the model was fit on (same columns).

B

Number of bootstrap resamples.

case, stratum

Event-indicator and stratum columns, resolved exactly as in rem() (defaults: the formula's left-hand side, and cumsum(case == 1)).

n_grid

Grid resolution for the partial-dependence curves.

level

Confidence level for the bands and the concordance interval.

seed

Optional integer seed for the resampling.

Value

An object of class "nn_uncertainty": a per-feature list of data.frame(x, lo, med, hi) bands, a concordance quantile interval, and the settings B, level. Has print() and plot() methods.

Details

Each bootstrap partial-dependence curve is centred (its grid-mean removed) before the pointwise quantiles are taken, so the bands describe uncertainty in the shape of each effect, not the conditional-logit's unidentified per-stratum offset.

See also

rem(), nn_control()