似乎很多人都有这个问题多年。有无数的问题解决这个问题。我已经尝试了他们建议的所有解决方案，但没有一个对我有效。这将是很好的知道这里的根本问题是什么，因为错误消息没有帮助。
Something is wrong; all the ROC metric values are missing:
caret - error - Something is wrong - all the ROC metric values are missing:
error in caret ROC metric : "Something is wrong; all the ROC metric values are missing"
Using metric ROC in caret train function in R
Issue using 'ROC' metric in caret train function in R
下面是我的代码中的一个可重复的例子。我不得不削减测试数据，但错误似乎是一样的。完整的数据集有44个预测因子而不是8个，1800个观察值而不是30个。

test_data <- structure(list(elevation = c(4L, 4L, 146L, 146L, 146L, 146L, 
146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 
146L, 146L, 146L, 146L, 146L, 204L, 291L, 291L, 291L, 291L, 413L, 
413L, 413L), stdev_elevation = c(0L, 0L, 3L, 3L, 3L, 2L, 3L, 
3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 40L, 
52L, 52L, 52L, 52L, 91L, 91L, 91L), d2coast = c(9L, 8L, 142L, 
142L, 140L, 137L, 139L, 140L, 140L, 135L, 135L, 140L, 135L, 137L, 
135L, 137L, 135L, 135L, 140L, 137L, 134L, 132L, 3L, 10L, 10L, 
10L, 10L, 7L, 7L, 7L), lc_class = structure(c(12L, 12L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 9L, 9L, 9L, 9L, 9L, 2L, 2L, 2L), levels = c("Cryptogam barren complex (bedrock)", 
"Cryptogam, herb barren", "Erect dwarf-shrub tundra", "Graminoid, prostrate dwarf-shrub, forb tundra", 
"Low-shrub tundra", "Nontussock sedge, dwarf-shrub, moss tundra", 
"Prostrate dwarf-shrub, herb tundra", "Prostrate/Hemiprostrate dwarf-shrub tundra", 
"Rush/grass, forb, cryptogam tundra", "Sedge, moss, dwarf-shrub wetland", 
"Sedge, moss, low-shrub wetland", "Sedge/grass, moss wetland", 
"Tussock-sedge, dwarf-shrub, moss tundra"), class = "factor"), 
    substrate = c(3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L), elevation2 = c(18L, 18L, 21386L, 21386L, 
    21465L, 21360L, 21427L, 21465L, 21465L, 21430L, 21430L, 21465L, 
    21430L, 21360L, 21430L, 21360L, 21430L, 21430L, 21465L, 21360L, 
    21380L, 21334L, 41625L, 84836L, 84836L, 84836L, 84836L, 170996L, 
    170996L, 170996L), stdev_elevation2 = c(0L, 0L, 13L, 13L, 
    10L, 8L, 10L, 10L, 10L, 7L, 7L, 10L, 7L, 8L, 7L, 8L, 7L, 
    7L, 10L, 8L, 6L, 5L, 1644L, 2723L, 2723L, 2723L, 2723L, 8418L, 
    8418L, 8418L), d2coast2 = c(81L, 77L, 20236L, 20236L, 19753L, 
    18932L, 19479L, 19753L, 19753L, 18449L, 18449L, 19753L, 18449L, 
    18932L, 18449L, 18932L, 18449L, 18449L, 19753L, 18932L, 18064L, 
    17678L, 11L, 100L, 100L, 100L, 100L, 52L, 52L, 52L), presence = c("no", 
    "yes", "no", "no", "no", "no", "no", "no", "no", "no", "no", 
    "no", "no", "no", "no", "no", "no", "no", "no", "no", "no", 
    "no", "no", "no", "no", "no", "no", "no", "no", "no"), Region_Code = c(3L, 
    3L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L)), row.names = c(NA, -30L), class = c("tbl_df", "tbl", 
"data.frame"))

这是我不喜欢的模特

library(caret)
library(CAST)
library(pROC)

test_data <- as.data.frame(test_data)

indices <- CreateSpacetimeFolds(test_data, spacevar = "Region_Code", k = 3)

pred <- test_data[,1:8]
obs <- test_data[,9] 

##### doesn't work

model1 <- ffs(predictors = pred, 
           response = obs,
           trControl = trainControl(method = 'cv', number = 12, summaryFunction = twoClassSummary, classProbs = TRUE,  savePredictions = TRUE),
           minVar = 2,
           method = 'glm', 
           family = 'binomial', 
           metric = 'ROC',
           index = indices$index)


#[1] "model using elevation,stdev_elevation will be trained now..."
#Something is wrong; all the ROC metric values are missing:
#      ROC           Sens          Spec    
# Min.   : NA   Min.   : NA   Min.   : NA  
# 1st Qu.: NA   1st Qu.: NA   1st Qu.: NA  
# Median : NA   Median : NA   Median : NA  
# Mean   :NaN   Mean   :NaN   Mean   :NaN  
# 3rd Qu.: NA   3rd Qu.: NA   3rd Qu.: NA  
# Max.   : NA   Max.   : NA   Max.   : NA  
# NA's   :1     NA's   :1     NA's   :1    
#Error: Stopping
#In addition: There were 13 warnings (use warnings() to see them)


##### works fine if we remove the index

model2 <- ffs(predictors = pred, 
              response = obs,
              trControl = trainControl(method = 'cv', number = 12, summaryFunction = twoClassSummary, classProbs = TRUE,  savePredictions = TRUE),
              minVar = 2,
              method = 'glm', 
              family = 'binomial', 
              metric = 'ROC')


[1] "model using elevation,stdev_elevation will be trained now..."
[1] "maximum number of models that still need to be trained: 48"
[1] "model using elevation,d2coast will be trained now..."
[1] "maximum number of models that still need to be trained: 47"
[1] "model using elevation,lc_class will be trained now..."
[1] "maximum number of models that still need to be trained: 46"
[1] "model using elevation,substrate will be trained now..."
[1] "maximum number of models that still need to be trained: 45"
[1] "model using elevation,elevation2 will be trained now..."
[1] "maximum number of models that still need to be trained: 44"
[1] "model using elevation,stdev_elevation2 will be trained now..."
[1] "maximum number of models that still need to be trained: 43"
[1] "model using elevation,d2coast2 will be trained now..."

"我只对两件事感兴趣"
1.为什么我会收到此型号特有的错误？
1.这个错误代码通常意味着什么？知道ROC指标缺失并没有帮助我或上面列出的stackoverflow问题中的人找出他们的模型中导致这个错误的错误。我还没有能够在所有建议的潜在解决方案中识别出一个共同的主题。

#Relevant session info:

> sessionInfo()
R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_Canada.utf8  LC_CTYPE=English_Canada.utf8    LC_MONETARY=English_Canada.utf8 LC_NUMERIC=C                    LC_TIME=English_Canada.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] pROC_1.18.0     CAST_0.7.1      caret_6.0-93    lattice_0.20-45 ggplot2_3.4.1  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0     terra_1.7-3          purrr_1.0.1          reshape2_1.4.4       listenv_0.9.0        splines_4.2.2        colorspace_2.1-0    
 [8] vctrs_0.5.2          generics_0.1.3       stats4_4.2.2         utf8_1.2.3           survival_3.4-0       prodlim_2019.11.13   rlang_1.0.6         
[15] ModelMetrics_1.2.2.2 pillar_1.8.1         glue_1.6.2           withr_2.5.0          foreach_1.5.2        lifecycle_1.0.3      plyr_1.8.8          
[22] lava_1.7.2.1         stringr_1.5.0        timeDate_4022.108    munsell_0.5.0        gtable_0.3.1         future_1.31.0        recipes_1.0.5       
[29] codetools_0.2-18     parallel_4.2.2       class_7.3-20         fansi_1.0.4          Rcpp_1.0.10          scales_1.2.1         ipred_0.9-13        
[36] parallelly_1.34.0    digest_0.6.31        stringi_1.7.12       dplyr_1.1.0          grid_4.2.2           hardhat_1.2.0        cli_3.6.0           
[43] tools_4.2.2          magrittr_2.0.3       tibble_3.1.8         future.apply_1.10.0  pkgconfig_2.0.3      MASS_7.3-58.1        Matrix_1.5-3        
[50] data.table_1.14.8    lubridate_1.9.2      timechange_0.2.0     gower_1.0.1          rstudioapi_0.14      iterators_1.0.14     R6_2.5.1            
[57] globals_0.16.2       rpart_4.1.19         nnet_7.3-18          nlme_3.1-160         compiler_4.2.2