Summarize a fitted Poisson Super Learner object

Prints:

1. a compact description of the fitted ensemble,

2. cross-validated deviances for base learners (when available),

3. cause-specific meta-learner coefficients (stacking weights).

Usage

# S3 method for class 'poisson_superlearner'
summary(object, cause = NULL, model = "sl", ...)

Arguments

object

poisson_superlearner 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"

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

"discrete_sl" and aliases

Summarize the cause-specific base learners with the smallest cross-validated deviance.

learner label

Summarize one stored base learner by its label in object$data_info$learners_labels[[k]].

"learner_j" or character integer "j"

Summarize the j-th stored learner.

integer j >= 1

Summarize the j-th stored learner.

vector of labels or positive integer indices

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

...

Passed to the underlying coef() method for the fitted meta-learner (learner-dependent; e.g. s for glmnet).

Value

Invisibly returns a list with elements:

cross_validation_deviance

data.table (or NULL).

meta_coefficients

List of length n_crisks with cause-specific coefficient objects (or NULL).

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
)

s <- summary(fit, cause = 1)
#> Call:
#>   Superlearner(..., metalearner = glmnet::glmnet)
#> 
#> Fitted object:
#>   Class: poisson_superlearner
#>   Number of competing risks: 2 
#>   Number of folds: 2 
#>   Maximum follow-up: 15.82977 
#>   Number of nodes: 5 
#> 
#> Retained learners by cause:
#>   cause 1: lasso, ridge
#>   cause 2: lasso, ridge
#> 
#> Cross-validation deviance:
#>    cause_index   cause learner_index learner  deviance
#>          <int>  <char>         <int>  <char>     <num>
#> 1:           1 cause_1             1   lasso 111.08762
#> 2:           1 cause_1             2   ridge 217.13423
#> 3:           2 cause_2             1   lasso  85.06296
#> 4:           2 cause_2             2   ridge  81.83562
#> 
#> Meta-learner coefficients:
#>   cause 1:
#> (Intercept)       lasso       ridge 
#>   0.0000000   1.7940042  -0.8157702 
#> 
names(s)
#> [1] "cross_validation_deviance" "meta_coefficients"