变异下标越界

我正在尝试从两个插入符号模型输出创建敏感性和特异性的汇总表，并且在我的 for 循环中，当从包含值的名为 models 的列表中查找值时，它会抛出下标越界错误？这是一个完全可重现的示例。 Dplyr 的版本是 0.7.0。谢谢。

heart <- read.table("http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/SAheart.data",
       sep=",",head=T,row.names=1)

library(car)

heart$chd <- factor(car::recode(heart$chd, "c(1)='Yes'; 
else='No'"), levels = c('Yes', 'No'))

library(caret)

set.seed(42)
index <- createDataPartition(heart$chd, p = 0.7, list = FALSE)
train_data <- heart[index, ]
test_data  <- heart[-index, ]
set.seed(42)
model_rf <- caret::train(chd ~ .,
                     data = train_data,
                     method = "rf",
                     trControl = trainControl(method = "repeatedcv", 
                                              number = 10, 
                                              repeats = 10, 
                                              verboseIter = FALSE))
final <- data.frame(actual = test_data$chd,
                predict(model_rf, newdata = test_data, type = "prob"))
final$predict <- factor(ifelse(final$Yes > 0.5, "Yes", "No"), levels = c('Yes', 'No'))
cm_original <- confusionMatrix(final$predict, test_data$chd)


# down sampling
ctrl <- trainControl(method = "repeatedcv", 
                 number = 10, 
                 repeats = 10, 
                 verboseIter = FALSE,
                 sampling = "down")

set.seed(42)
model_rf_under <- caret::train(chd ~ .,
                     data = train_data,
                     method = "rf",

                     trControl = ctrl)

final_under <- data.frame(actual = test_data$chd,
                predict(model_rf_under, newdata = test_data, type = "prob"))
final_under$predict <- factor(ifelse(final_under$Yes > 0.5, "Yes", "No"), levels = c('Yes', 'No'))
cm_down <- confusionMatrix(final_under$predict, test_data$chd)


models <- list(original = model_rf,
           under = model_rf_under
           )

library(dplyr)
comparison <- data.frame(model = names(models),
                     Sensitivity = rep(NA, length(models)),
                     Specificity = rep(NA, length(models))
                      )

for (name in names(models)) {
model <- get(paste0("cm_", name))

comparison[comparison$model == name, ] <- filter(comparison, model == 
name) %>% mutate( Sensitivity = model[['byClass']][['Sensitivity']],
                 Specificity = model[['byClass']][['Specificity']]  )

}

comparison 

对此的回答很晚，但以防万一有人遇到这个问题。

我遇到了类似的问题。尽管有错误mutate在管道之后。

或者我用过mutate_()这解决了问题。

filter(comparision, model == name) %>% 
 mutate_(Sensitivity = model[["byClass"]][['Sensitivity']],
 Specificity = model[["byClass"]][['Specificity']])