Print method for poisson_superlearner

Prints a compact description of the fitted Poisson Super Learner, including the number of base learners, the meta-learner, the time-grid used, and competing-risk structure. Optionally prints the fitted meta-learner for a given cause.

Usage

# S3 method for class 'poisson_superlearner'
print(x, cause = 1, model = "sl", ...)

Arguments

x

poisson_superlearner object returned by Superlearner().

cause

numeric(1) or NULL. Which cause's meta-learner fit to print. If NULL, prints one line per cause (classes only) instead of printing the full fitted objects.

model

Model selector. Default is "sl" for the stacked super learner. Allowed values are:

0, "sl", "superlearner", or "super_learner"

Print the stacked meta-learner. For causes with no fitted meta-learner, this falls back to the retained base learner.

"discrete_sl" and aliases

Print the cause-specific base learner with the smallest cross-validated deviance.

learner label

Print one stored base learner by its label in x$data_info$learners_labels[[k]].

"learner_j" or character integer "j"

Print the j-th stored learner.

integer j >= 1

Print the j-th stored learner.

vector of labels or positive integer indices

Use cause-specific base learners; length must equal x$data_info$n_crisks.

...

Passed to the underlying fitted meta-learner print() method when cause is a single integer.

Value

Invisibly returns x.

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
)

print(fit, cause = NULL)
#> 
#> Cause 1, model = sl
#> 
#> Call:  glmnet::glmnet(x = x_meta, y = as.numeric(level_one_data[["deltaij"]][ok]),      family = "poisson", offset = log(level_one_data[["tij"]][ok]),      lambda = 0, standardize = FALSE, intercept = FALSE) 
#> 
#>   Df  %Dev Lambda
#> 1  2 82.33      0
#> 
#> Cause 2, model = sl
#> 
#> Call:  glmnet::glmnet(x = x_meta, y = as.numeric(level_one_data[["deltaij"]][ok]),      family = "poisson", offset = log(level_one_data[["tij"]][ok]),      lambda = 0, standardize = FALSE, intercept = FALSE) 
#> 
#>   Df %Dev Lambda
#> 1  2 92.2      0