Returns the predicted hazard (response scale) as a plain numeric vector, one
value per row of newdata. Together with sim_hazard this
is the only primitive a new estimation backend must provide: every derived
quantity (cumulative hazard, survival probability, CIF, transition
probabilities) and its simulation-based confidence intervals are built from
these two. Analytic ("default"/"delta") confidence intervals
additionally use make_X/get_coefs/get_Vp.
get_hazard(object, newdata, ...)# S3 method for default
get_hazard(object, newdata, ...)
A numeric vector of hazards on the response scale.
A fitted model object.
A data frame for which the hazard is predicted.
Further arguments passed to methods.
The package website (https://adibender.github.io/pammtools/)
has worked examples of implementing get_hazard and
sim_hazard for new estimation backends: the articles
“Defining a new backend: gradient boosting with xgboost” (a bootstrap
tree ensemble) and “Bayesian Baseline PAMMs” (a brms model,
drawing from the posterior).