Stack(), blending_predictions of models, result in warning: 需要相关计算，但估计是常数，标准差为 0

如何解决Stack(), blending_predictions of models, result in warning: 需要相关计算，但估计是常数，标准差为 0

我第一次使用 stacks 包在集成模型中堆叠我的回归模型。一切都相对顺利，直到我到达需要混合预测的步骤，使用来自不同子模型的 blend_predictions()。我怀疑这与惩罚设置有关，因为显然 stacks() 没有选择模型，但是当我尝试更改它们时没有区别。我正在遵循 Andrew Couch ( https://github.com/andrew-couch/Tidy-Tuesday/blob/master/Season%201/Scripts/TidyModelsStacks.Rmd ) 的优秀示例，但是我不明白我遇到的错误。

我在写这个问题时正在处理 reprex，但如果有人有解释此错误消息的建议，我将不胜感激。谢谢。

era.af_stack_blend_fit <- era.af_stack %>% 
  blend_predictions(penalty = 0.01,mixture = 1)

警告信息

! Bootstrap01: internal: A correlation computation is required,but `estimate` is constant and has 0 standard deviation,resulting in a divide by 0 error. `NA...
! Bootstrap02: internal: A correlation computation is required,resulting in a divide by 0 error. `NA...
! Bootstrap03: internal: A correlation computation is required,resulting in a divide by 0 error. `NA...
....continues to 
! Bootstrap25: internal: A correlation computation is required,resulting in a divide by 0 error. `NA...

我的堆叠看起来像这样

era.af_stack <- stacks() %>% 
  add_candidates(lm_fit_cv) %>% 
  add_candidates(glm_fit_cv) %>%
  add_candidates(spline_fit_cv) %>%
  add_candidates(knn_fit_cv) %>%
  add_candidates(pca_fit_cv) %>%
  add_candidates(rf_resample_param_fit) %>%
  add_candidates(xgboost_res_param_fit)

# A data stack with 7 model definitions and 15 candidate members:
#   lm_fit_cv: 1 model configuration
#   glm_fit_cv: 1 model configuration
#   spline_fit_cv: 1 model configuration
#   knn_fit_cv: 1 model configuration
#   pca_fit_cv: 1 model configuration
#   rf_resample_param_fit: 5 model configurations
#   xgboost_res_param_fit: 5 model configurations
# Outcome: logRR (numeric)

era.af_stack_blend_fit
-- A stacked ensemble model -------------------------------------

Out of 15 possible candidate members,the ensemble retained 0.
Penalty: 0.01.
Mixture: 1.

The 0 highest weighted members are:
# A tibble: 0 x 3
# ... with 3 variables: member <chr>,type <chr>,weight <dbl>

Members have not yet been fitted with `fit_members()`.