rm(list = ls()) options(stringsAsFactors = F) Sys.setenv(LANGUAGE = "en") library(tidyverse) library(tidymodels) library(censored) #data set.seed(2022) data <- SimSurv(2000) %>% mutate(X2 = X2 + 1) head(data) data_split <- initial_split(data, strata = event, prop = 0.7) data_train <- training(data_split) data_test <- testing(data_split) #model cox_spec <- proportional_hazards(penalty = tune(),mixture = 1) %>% set_engine("glmnet") #resample cv <- vfold_cv(data = data_train, v = 10) #grid lambda_grid <- grid_regular(penalty(), levels = 50) #workflow wf <- workflow() %>% add_formula(Surv(time, event) ~ X1 + X2) #training doParallel::registerDoParallel() set.seed(2022) lasso_grid <- tune_grid( wf %>% add_model(cox_spec), resamples = cv, grid = lambda_grid) #Error in `check_metrics()`: #! Unknown `mode` for parsnip model.
That's next on our list; censored isn't 100% integrated into tidymodels. We need to set up performance statistics and a few other things before that will work
Thanks a lot for your answer. Now the tidymodels package and mlr3 package in the R language are the two camps for machine learning algorithms.As far as I know, the mlr3 package has been able to complete the above algorithm and published the corresponding article, I very much hope that the tidymodels package can also be updated as soon as possible to support the survival data machine learning algorithm.
The following is the name of the article and its code address
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