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 withmethod = "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, andcumsum(case == 1)).- n_grid
Number of grid points of each partial-dependence curve, which spans the 1st to 99th percentile of the feature in
data.- 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.