R 列车（）中的公式与非公式接口

[我研究了类似的线索和问题，Max和其他人提出的问题似乎都与我的案件无关。]

我在这里看到一些关于公式接口失败的报道，而非公式接口对他们来说运行良好。我的问题恰恰相反。下面带有公式界面的

train（）

函数工作正常：

glmTune <- train(class ~ .,
                 data = trainData,
                 method = "glmnet",
                 trControl = train.control,
                 tuneGrid = tune.grid)

对于数值

y

而言，它只是略有不同（不同的性能指标）：

代码如下：

library(caret)
library(dplyr)
library(glmnet)

# see dput(droplevels(head(df, 20))) output of data below:

# 70%/30% split
set.seed(42)
inTrain <- createDataPartition(df$lnprice, p=0.7, list=F)
trainData <- df[inTrain, ]
testData <- df[-inTrain, ]

# train model
train.control <- trainControl(method = "repeatedcv",
                              number = 10,
                              repeats= 5,
                              allowParallel = F)
tune.grid <- expand.grid(lambda = seq(0.0001,0.1,length=20),
                         alpha = c(0, 0.5, 1))
X <- trainData[, !(names(trainData) %in% "lnprice")]
Y <- trainData$lnprice
fit <- train(
#  x = X, y = Y,                        # non-formula
  lnprice ~ ., data = trainData,       # formula
  method = "glmnet",
  preProcess = c("zv", "center", "scale"),
  tuneGrid = tune.grid,
  trControl = train.control)

# plot model
print(plot(fit))

> dput(droplevels(head(df,20)))
structure(list(fuel.type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "gas", class = "factor"), 
    aspiration = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("std", 
    "turbo"), class = "factor"), num.of.doors = structure(c(2L, 
    2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 
    2L, 1L, 2L, 2L), .Label = c("four", "two"), class = "factor"), 
    body.style = structure(c(1L, 1L, 2L, 3L, 3L, 3L, 3L, 4L, 
    3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L), .Label = c("convertible", 
    "hatchback", "sedan", "wagon"), class = "factor"), drive.wheels = structure(c(2L, 
    2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 1L), .Label = c("fwd", "rwd", "X4wd"), class = "factor"), 
    engine.location = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "front", class = "factor"), 
    wheel.base = c(88.6, 88.6, 94.5, 99.8, 99.4, 99.8, 105.8, 
    105.8, 105.8, 99.5, 101.2, 101.2, 101.2, 101.2, 103.5, 103.5, 
    103.5, 110, 88.4, 94.5), length = c(168.8, 168.8, 171.2, 
    176.6, 176.6, 177.3, 192.7, 192.7, 192.7, 178.2, 176.8, 176.8, 
    176.8, 176.8, 189, 189, 193.8, 197, 141.1, 155.9), width = c(64.1, 
    64.1, 65.5, 66.2, 66.4, 66.3, 71.4, 71.4, 71.4, 67.9, 64.8, 
    64.8, 64.8, 64.8, 66.9, 66.9, 67.9, 70.9, 60.3, 63.6), height = c(48.8, 
    48.8, 52.4, 54.3, 54.3, 53.1, 55.7, 55.7, 55.9, 52, 54.3, 
    54.3, 54.3, 54.3, 55.7, 55.7, 53.7, 56.3, 53.2, 52), curb.weight = c(2548L, 
    2548L, 2823L, 2337L, 2824L, 2507L, 2844L, 2954L, 3086L, 3053L, 
    2395L, 2395L, 2710L, 2765L, 3055L, 3230L, 3380L, 3505L, 1488L, 
    1874L), engine.type = structure(c(1L, 1L, 4L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L), .Label = c("dohc", 
    "l", "ohc", "ohcv"), class = "factor"), num.of.cylinders = structure(c(2L, 
    2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L), .Label = c("five.six", "four.or.less"), class = "factor"), 
    engine.size = c(130L, 130L, 152L, 109L, 136L, 136L, 136L, 
    136L, 131L, 131L, 108L, 108L, 164L, 164L, 164L, 209L, 209L, 
    209L, 61L, 90L), fuel.system = structure(c(1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L), .Label = c("mpfi", "X2bbl"), class = "factor"), bore = c(3.47, 
    3.47, 2.68, 3.19, 3.19, 3.19, 3.19, 3.19, 3.13, 3.13, 3.5, 
    3.5, 3.31, 3.31, 3.31, 3.62, 3.62, 3.62, 2.91, 3.03), stroke = c(2.68, 
    2.68, 3.47, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 2.8, 2.8, 
    3.19, 3.19, 3.19, 3.39, 3.39, 3.39, 3.03, 3.11), compression.ratio = c(9, 
    9, 9, 10, 8, 8.5, 8.5, 8.5, 8.3, 7, 8.8, 8.8, 9, 9, 9, 8, 
    8, 8, 9.5, 9.6), horsepower = c(111, 111, 154, 102, 115, 
    110, 110, 110, 140, 160, 101, 101, 121, 121, 121, 182, 182, 
    182, 48, 70), peak.rpm = c(5000L, 5000L, 5000L, 5500L, 5500L, 
    5500L, 5500L, 5500L, 5500L, 5500L, 5800L, 5800L, 4250L, 4250L, 
    4250L, 5400L, 5400L, 5400L, 5100L, 5400L), city.mpg = c(21L, 
    21L, 19L, 24L, 18L, 19L, 19L, 19L, 17L, 16L, 23L, 23L, 21L, 
    21L, 20L, 16L, 16L, 15L, 47L, 38L), highway.mpg = c(27L, 
    27L, 26L, 30L, 22L, 25L, 25L, 25L, 20L, 22L, 29L, 29L, 28L, 
    28L, 25L, 22L, 22L, 20L, 53L, 43L), make = structure(c(1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L), .Label = c("alfa.romero", "audi", "bmw", 
    "chevrolet"), class = "factor"), lnprice = c(9.5101, 9.7111, 
    9.7111, 9.5432, 9.7671, 9.6323, 9.7819, 9.848, 10.0806, 9.69176, 
    9.7069, 9.7365, 9.9508, 9.9573, 10.1091, 10.334, 10.629, 
    10.5154, 8.5469, 8.7475)), .Names = c("fuel.type", "aspiration", 
"num.of.doors", "body.style", "drive.wheels", "engine.location", 
"wheel.base", "length", "width", "height", "curb.weight", "engine.type", 
"num.of.cylinders", "engine.size", "fuel.system", "bore", "stroke", 
"compression.ratio", "horsepower", "peak.rpm", "city.mpg", "highway.mpg", 
"make", "lnprice"), row.names = c(NA, 20L), class = "data.frame")

库（插入符号）
图书馆（dplyr）
图书馆（glmnet）
#见以下数据的dput（液滴液位（水头（df，20）））输出：
#70%/30%分割
种子（42）
inTrain奇怪。看起来
train.default
方法没有默认的
na.action
处理程序

从
？插入符号：：序列的输出

## Default S3 method:
train(x, y, method = "rf", preProcess = NULL, ...,
  weights = NULL, metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
  maximize = ifelse(metric %in% c("RMSE", "logLoss", "MAE"), FALSE, TRUE),
  trControl = trainControl(), tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3))

而
train.formula
方法的作用是：

## S3 method for class 'formula'
train(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL)
                                        ^^^^^^^^^^^^^^^^^^^

如果您将
na.action=na.fail
添加到您的
train.default
调用，
x，y
接口，您是否会得到与
train.formula
调用相同的行为？
示例数据集中没有任何NAs，但我仍然可以重现错误。我认为问题在于glmnet方法只需要数字变量，而
train
将使用公式方法为因子创建虚拟变量，但不使用x，y规范（请参阅）

下面我使用recipes包从因子变量中创建一个热编码变量。我删除了变量fuel.type和engine.location，因为它们在您的小示例数据集中只有一个级别

trainData <- trainData %>% select(-fuel.type, -engine.location)
rec <- recipe(lnprice~., data = trainData) %>% step_dummy(all_predictors(), one_hot = TRUE)
rec_prep <- prep(rec, trainData)
train_new <- bake(rec_prep, trainData)
X <- as.data.frame(train_new[, !(names(train_new) %in% "lnprice")])
Y <- train_new$lnprice

fit <- train(
  x = X, y = Y,                        # non-formula
  #lnprice ~ ., data = trainData,       # formula
  method = "glmnet",
  preProcess = c("zv", "center", "scale"),
  tuneGrid = tune.grid,
  trControl = train.control)

trainData%选择（-燃料类型，-发动机位置）
记录%step\u dummy（所有预测值（），一个热=真）
rec_prep谢谢你调查这件事。我刚试过你的建议。完全相同的NA错误。如果要忽略
NA
s，可以使用
NA.action=NA.pass
。但是没有真正解释为什么这两种方法处理的方式不同对不起，很抱歉回复太晚-我再次检查了
na.action=na.pass，
相同的错误。不知道这些错误消息（添加在我的原始帖子中）是否包含一些线索。再次感谢。na.action=na.pass将把NAs传递给模型，而不是忽略它们。你需要na.action=na.omit。你能给我们一个小的，可重复的例子来测试吗？我已经把代码贴在了我原来的帖子下面。这适用于使用自动定价数据的回归模型（log（price），存储在“lnprice”列中，是响应变量）。完整的数据有205行和24列，我不知道如何给您提供数据，因此我尝试按照说明，使用dput（droplevels（head（df，20））的R-console输出粘贴前20行。
希望这有帮助。非常感谢你调查这件事。
## Default S3 method:
train(x, y, method = "rf", preProcess = NULL, ...,
  weights = NULL, metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
  maximize = ifelse(metric %in% c("RMSE", "logLoss", "MAE"), FALSE, TRUE),
  trControl = trainControl(), tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3))

## S3 method for class 'formula'
train(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL)
                                        ^^^^^^^^^^^^^^^^^^^

trainData <- trainData %>% select(-fuel.type, -engine.location)
rec <- recipe(lnprice~., data = trainData) %>% step_dummy(all_predictors(), one_hot = TRUE)
rec_prep <- prep(rec, trainData)
train_new <- bake(rec_prep, trainData)
X <- as.data.frame(train_new[, !(names(train_new) %in% "lnprice")])
Y <- train_new$lnprice

fit <- train(
  x = X, y = Y,                        # non-formula
  #lnprice ~ ., data = trainData,       # formula
  method = "glmnet",
  preProcess = c("zv", "center", "scale"),
  tuneGrid = tune.grid,
  trControl = train.control)