Get the risk set at each bin over an equal distance grid
get_risk_obj(Y, by, max_T, id, is_for_discrete_model = T, n_threads = 1,
min_chunk = 5000)Vector of outcome variable
Length of each bin
Last observed time
Vector with ids where entries match with outcomes Y
TRUE/FALSE for whether the model outcome is discrete. For example, a logit model is discrete whereas what is coined an exponential model in this package is a dynamic model
Set to a value greater than one to use mclapply to find the risk object
Minimum chunk size of ids to use when parallel version is used.
A list with the following elements:
risk_setsList of lists with one for each bin. Each of the sub lists have indices that corresponds to the entries of Y that are at risk in the bin
min_startStart time of the first bin
I_lenLength of each bin
dNumber of bins
is_event_inIndices for which bin an observation Y is an event. -1 if the individual does not die in any of the bins
is_for_discrete_modelValue of is_for_discrete_model argument