glmnet breaks in tune

[hfrick]

This looks like a bug and needs a bunch of different things to come together but since one of them is "glmnet", I'm guessing it's a censored issue.

# stackoverflow post -----------------------------------------------------

# Source - https://stackoverflow.com/q/79929921
# Posted by Lukas D. Sauer
# Retrieved 2026-04-22, License - CC BY-SA 4.0

# minimal reprex

library(tidymodels)
library(censored)
#> Loading required package: survival
n <- 800

set.seed(403)
df <- data.frame(
  treatment = sample(c("A", "B", "C"), n, replace = TRUE),
  age = runif(n, 18, 99),
  #sex = sample(c("m", "f"), n, replace = TRUE),
  time = rexp(n),
  event = sample(c(0, 1), n, replace = TRUE)
) %>%
  filter(time > 0) %>%
  mutate(event_surv = Surv(time, event), .keep = "unused")

cv <- vfold_cv(df, v = 10)

spec <- proportional_hazards(penalty = 0.1, mixture = 1) %>%
  set_engine("glmnet") %>%
  set_mode("censored regression")

rec <- recipe(event_surv ~ ., data = df)

wflow <- workflow() %>%
  add_recipe(rec) %>%
  add_model(spec)

res <- fit_resamples(
  wflow,
  resamples = cv,
  metrics = metric_set(concordance_survival)
)
#> → A | error:   error in evaluating the argument 'x' in selecting a method for function 'as.matrix': non-conformable arguments
#> There were issues with some computations   A: x1
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.
#> There were issues with some computations   A: x10
#> 


#  how we got here -------------------------------------------------------

# has to be a recipe because formula works
res <- workflow() %>%
  add_formula(event_surv ~ .) %>%
  add_model(spec) %>%
  fit_resamples(
    resamples = cv,
    metrics = metric_set(concordance_survival)
  )

# has to be resampling because a single fit works
res <- workflow() %>%
  add_recipe(rec) %>%
  add_model(spec) %>%
  fit(df)

# has to be glmnet because a different model works
res <- workflow() %>%
  add_recipe(rec) %>%
  add_model(survival_reg()) %>%
  fit_resamples(
    resamples = cv,
    metrics = metric_set(concordance_survival)
  )

# has to do with nominal predictor because two numeric predictors work
set.seed(403)
df <- data.frame(
  #treatment = sample(c("A", "B", "C"), n, replace = TRUE),
  age = runif(n, 18, 99),
  age2 = runif(n, 30, 100),
  #sex = sample(c("m", "f"), n, replace = TRUE),
  time = rexp(n),
  event = sample(c(0, 1), n, replace = TRUE)
) %>%
  filter(time > 0) %>%
  mutate(event_surv = Surv(time, event), .keep = "unused")

res <- workflow() %>%
  add_recipe(recipe(event_surv ~ ., data = df)) %>%
  add_model(spec) %>%
  fit_resamples(
    resamples = vfold_cv(df, v = 10),
    metrics = metric_set(concordance_survival)
  )

# has to include `treatment` for that particular error
# keeping only `sex` produces warning about NA coercion
# (can't remove age because glmnet needs at least 2 predictors)
set.seed(403)
df <- data.frame(
  #treatment = sample(c("A", "B", "C"), n, replace = TRUE),
  age = runif(n, 18, 99),
  sex = sample(c("m", "f"), n, replace = TRUE),
  time = rexp(n),
  event = sample(c(0, 1), n, replace = TRUE)
) %>%
  filter(time > 0) %>%
  mutate(event_surv = Surv(time, event), .keep = "unused")

res <- workflow() %>%
  add_recipe(recipe(event_surv ~ ., data = df)) %>%
  add_model(spec) %>%
  fit_resamples(
    resamples = vfold_cv(df, v = 10),
    metrics = metric_set(concordance_survival)
  )
#> → A | warning: NAs introduced by coercion
#> There were issues with some computations   A: x8
#> There were issues with some computations   A: x10
#> 

^{Created on 2026-04-22 with reprex v2.1.1}