Calculate predicted hazard

get_hazard(object, newdata, ...)

# S3 method for default
get_hazard(
  object,
  newdata,
  reference = NULL,
  ci = TRUE,
  type = c("response", "link"),
  ci_type = c("default", "delta", "sim"),
  time_var = NULL,
  se_mult = 2,
  ...
)

Arguments

object: a fitted gam object as produced by gam().
newdata: A data frame or list containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. If newdata is provided then it should contain all the variables needed for prediction: a warning is generated if not. See details for use with link{linear.functional.terms}.
...: Further arguments passed to predict.gam and get_hazard
reference: A data frame with number of rows equal to nrow(newdata) or one, or a named list with (partial) covariate specifications. See examples.
ci: logical. Indicates if confidence intervals should be calculated. Defaults to TRUE.
type: Either "response" or "link". The former calculates hazard, the latter the log-hazard.
ci_type: The method by which standard errors/confidence intervals will be calculated. Default transforms the linear predictor at respective intervals. "delta" calculates CIs based on the standard error calculated by the Delta method. "sim" draws the property of interest from its posterior based on the normal distribution of the estimated coefficients. See here for details and empirical evaluation.
time_var: Name of the variable used for the baseline hazard. If not given, defaults to "tend" for gam fits, else "interval". The latter is assumed to be a factor, the former numeric.
se_mult: Factor by which standard errors are multiplied for calculating the confidence intervals.