Absolute-risk matrix predictions for a fitted base learner

Usage

# S3 method for class 'base_learner'
predictRisk(object, newdata, times, cause = 1, ...)

Arguments

object: base_learner. Fitted object from fit_learner().
newdata: data.frame. New covariate data.
times: numeric. Prediction times.
cause: numeric(1). Cause index.
...: Unused.

Value

numeric matrix with nrow(newdata) rows and length(times) columns.

Examples

d <- simulateStenoT1(30, competing_risks = TRUE)
lrn <- Learner_glmnet(
  covariates = c("sex", "value_LDL"),
  lambda = 0.01,
  cross_validation = FALSE
)
bl <- fit_learner(
  d,
  learner = lrn,
  id = "id",
  status = "status_cvd",
  event_time = "time_cvd",
  number_of_nodes = 3
)

if (requireNamespace("riskRegression", quietly = TRUE)) {
  riskRegression::predictRisk(bl, newdata = d[1:3], times = c(1, 3), cause = 1)
}
#>            [,1]      [,2]
#> [1,] 0.07174035 0.1992843
#> [2,] 0.14741794 0.3796804
#> [3,] 0.09266836 0.2521300