See the vignette 'Bootstrap illustration'. The
do_stratify_with_event may be useful when either cases or non-cases are very rare to ensure that the model estimation succeeds.
ddhazard_boot(ddhazard_fit, strata, unique_id, R = 100, do_stratify_with_event = F, do_sample_weights = F, LRs = ddhazard_fit$control$LR * 2^(0:(-4)), print_errors = F)
Returned object from a
Strata to sample within. These need to be on an individual by individual basis and not rows in the design matrix
Unique ids where entries match entries of
Number of bootstrap estimates
TRUEif sampling should be by strata of whether the individual has an event. An interaction factor will be made if
TRUEif weights should be sample instead of individuals
Learning rates in decreasing order which will be used to estimate the model.
TRUEif errors should be printed when estimations fails
An object like returned from the