ddhazard
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 ddhazard
call
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 strata
Number of bootstrap estimates
TRUE
if sampling should be by strata of whether the individual has an event. An interaction factor will be made if strata
is provided
TRUE
if weights should be sample instead of individuals
Learning rates in decreasing order which will be used to estimate the model.
TRUE
if errors should be printed when estimations fails
An object like returned from the boot
function