Extract stacking (meta-learner) coefficients from a fitted Poisson Super Learner

Extracts the meta-learner coefficients (stacking weights) from a fitted poisson_superlearner object returned by Superlearner().

Usage

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

Arguments

object

poisson_superlearner. A fitted ensemble returned by Superlearner().

cause

numeric(1) or NULL. Which cause to extract meta-learner coefficients for. If NULL, coefficients are returned for all causes. Causes are indexed 1, 2, ..., object$data_info$n_crisks.

model

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

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

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

"discrete_sl" and aliases

Extract coefficients from the cause-specific base learners with the smallest cross-validated deviance.

learner label

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

"learner_j" or character integer "j"

Extract coefficients from the j-th stored learner.

integer j >= 1

Extract coefficients from 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 of the fitted meta-learner (learner-dependent; e.g., s for glmnet).

Value

If cause is a single integer, returns the coefficient object produced by coef() for the selected cause-specific fitted model: the meta-learner when model = "sl" and a meta-learner is available, or the selected base learner when model selects a base learner or no meta-learner is available.

If cause = NULL, returns a list of length object$data_info$n_crisks, where element [[k]] contains coefficients for the selected model for cause k.

If no fitted ensemble is present (object$superlearner is NULL), signals a message and returns invisible(object).

Details

For each cause k, the ensemble stores a fitted meta-learner in object$superlearner[[k]]$meta_learner_fit. This method dispatches to the underlying coef() method for that fitted meta-learner.

What coefficients represent. These coefficients correspond to the meta-learner regression of the outcome on the cross-validated base-learner predictions (Z1, Z2, ...). Under the default meta-learner, they are the stacking weights (on the scale defined by the meta-learner).

Learner-dependent output. The returned coefficient object depends on the meta-learner implementation (by default a glmnet fit, often returning a sparse matrix). This method does not rename Z* terms or post-process coefficients; it returns the output of coef(object$superlearner[[k]]$meta_learner_fit, ...) unchanged.

Single-learner special case. If the ensemble was fit with only one base learner, no meta-learner is fit and meta_learner_fit is NULL. In that case, coef() for the poisson_superlearner does not have meta-learner coefficients to return.

Examples

d <- simulateStenoT1(50, competing_risks = TRUE)
learners <- list(
  glm = Learner_glmnet(covariates = c("age", "value_LDL"), lambda = 0, cross_validation = FALSE),
  gam = Learner_gam(covariates = c("age", "value_LDL"))
)
fit <- Superlearner(d, id="id", status="status_cvd", event_time="time_cvd",
                    learners=learners, number_of_nodes=4, nfold=2)

# meta-learner coefficients (cause 1)
coef(fit, cause = 1)
#> 3 x 1 sparse Matrix of class "dgCMatrix"
#>                     s0
#> (Intercept) .         
#> Z1          0.90743467
#> Z2          0.04019941

# meta-learner coefficients for all causes (list)
coef(fit)
#> $cause_1
#> 3 x 1 sparse Matrix of class "dgCMatrix"
#>                     s0
#> (Intercept) .         
#> Z1          0.90743467
#> Z2          0.04019941
#> 
#> $cause_2
#> 3 x 1 sparse Matrix of class "dgCMatrix"
#>                     s0
#> (Intercept)  .        
#> Z1           1.4696812
#> Z2          -0.6260814
#>