Absolute-risk matrix predictions for a fitted Poisson Super Learner — predictRisk.poisson

S3 method compatible with riskRegression::predictRisk returning one column per requested time.

Usage

# S3 method for class 'poisson_superlearner'
predictRisk(object, newdata, times, cause = 1, model = "sl", ...)

Arguments

object: poisson_superlearner. Fitted object.
newdata: data.frame. New covariate data.
times: numeric. Prediction times.
cause: numeric(1). Cause index.
model: Model selector. Default is "sl". Allowed values are the same as in predict.poisson_superlearner(), including "discrete_sl" and cause-specific vectors of base-learner labels or indices.
...: Unused.

Value

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

Examples

d <- simulateStenoT1(30, competing_risks = TRUE)

learners <- list(
  lasso = Learner_glmnet(
    covariates = "sex",
    alpha = 1,
    lambda = 0.01,
    cross_validation = FALSE
  ),
  ridge = Learner_glmnet(
    covariates = c("sex", "value_LDL"),
    alpha = 0,
    lambda = 0.01,
    cross_validation = FALSE
  )
)

fit <- Superlearner(
  data = d,
  id = "id",
  status = "status_cvd",
  event_time = "time_cvd",
  learners = learners,
  number_of_nodes = 3,
  nfold = 2
)

if (requireNamespace("riskRegression", quietly = TRUE)) {
  riskRegression::predictRisk(fit, newdata = d[1:3], times = c(1, 3), cause = 1)
}
#>            [,1]      [,2]
#> [1,] 0.09155551 0.2400111
#> [2,] 0.12180318 0.3137609
#> [3,] 0.11323322 0.2944297