Extract coefficients from a fitted base learner — coef.base

Convenience method to extract (cause-specific) model coefficients from a fitted base_learner returned by fit_learner().

Usage

# S3 method for class 'base_learner'
coef(object, cause = NULL, ...)

Arguments

object: base_learner. A fitted object returned by fit_learner().
cause: numeric(1) or NULL. Which cause to extract coefficients for. If NULL, coefficients are returned for all causes. Causes are indexed 1, 2, ..., object$data_info$n_crisks (with 0 reserved for censoring).
...: Passed to the underlying coef() method of the fitted learner object (learner-dependent; e.g., s for glmnet).

Value

If cause is a single integer, returns the coefficient object produced by coef() for that cause-specific fitted model.

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

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

Details

For competing risks, fit_learner() fits one model per cause, stored in object$learner_fit[[k]] for k = 1, 2, ..., K. This method simply dispatches to the underlying model’s coef() method for each fitted object.

Learner-dependent output. The returned coefficient object depends on the base learner used (e.g. a numeric vector, a sparse matrix, a list, etc.). This method does not post-process or rename coefficients; it returns the output of coef(object$learner_fit[[k]], ...) unchanged.

Examples

d <- simulateStenoT1(30, competing_risks = TRUE)
lrn <- Learner_glmnet(covariates = c("age", "value_LDL"),
                      lambda = 0, cross_validation = FALSE)
bl <- fit_learner(d, learner = lrn, id = "id",
                  status = "status_cvd", event_time = "time_cvd",
                  number_of_nodes = 4)

# coefficients for cause 1
coef(bl, cause = 1)
#> 8 x 1 sparse Matrix of class "dgCMatrix"
#>                      s0
#> (Intercept) -7.27079032
#> age          0.07701094
#> value_LDL    0.20879304
#> noden2       1.04966630
#> noden3       0.98499689
#> noden4       0.60121570
#> noden5      -7.09418083
#> noden6       .         

# coefficients for all causes (list)
coef(bl)
#> $`1`
#> 8 x 1 sparse Matrix of class "dgCMatrix"
#>                      s0
#> (Intercept) -7.27079032
#> age          0.07701094
#> value_LDL    0.20879304
#> noden2       1.04966630
#> noden3       0.98499689
#> noden4       0.60121570
#> noden5      -7.09418083
#> noden6       .         
#> 
#> $`2`
#> 8 x 1 sparse Matrix of class "dgCMatrix"
#>                      s0
#> (Intercept) -296.632806
#> age            6.003270
#> value_LDL    -27.179372
#> noden2        -2.334743
#> noden3        -1.288138
#> noden4        61.936066
#> noden5        57.543521
#> noden6         .       
#>