I want to predict the response of predicted survival times in survival analysis. According to the book mlr3 for comparing the predictions from the model to the true data (https://mlr3book.mlr-org.com/chapters/chapter13/beyond_regression_and_classification.html#learnersurv-predictionsurv-and-predict-types).

In the below, I’m training and predicting survival from an xgboost (lrn(“surv.xgboost.cox”)), and I’ve used type = “regression” for survival time prediction.

But It seems that the argument ‘type’ is not a valid argument for Learner ‘Surv.Xgboost.Cox’.

**How can I compare the predicted survival times from the model to the true survival times data?
**

library(mlr3proba)
#> Loading required package: mlr3

library(mlr3extralearners)
library(mlr3pipelines)
library(mlr3verse)
task = as_task_surv(x = survival::veteran, time = 'time', event = 'status')
poe = po('encode')
task = poe$train(list(task))[[1]]
set.seed(42)
part = partition(task, ratio = 0.8)
pred_xgb = lrn("surv.xgboost.cox", type = "regression")$
  train(task, split$train)$predict(task, split$test)
#> Error: Cannot set argument 'type' for 'LearnerSurvXgboostCox' (not a constructor argument, not a parameter, not a field. Did you mean 'normalize_type' / 'process_type' / 'sample_type'?

data.frame(pred = pred_xgb$response[1:3],
           truth = pred_xgb$truth[1:3])
#> Error in eval(expr, envir, enclos): object 'pred_xgb' not found

^{Created on 2024-05-24 with reprex v2.1.0}