当对 kNN 使用 `caret` 时，我得到“错误 in terms.formula(formula, data = data): '.' 在公式中，没有“数据”参数”

Question

我正在尝试使用caret为某些数据的 kNN 分析找到最佳k ：

library(tidyverse)
library(caret)

# Read and clean up the data
ugriz  <- read.table("QSOs_1st_50k.dat-mags.dat")
ugriz[ugriz == -999] <- NA
fields <- c('name', 'z','delta_z','NED_class','SDSS_class','no_radio','radio_max','no_UV', 'UV_min',
    'u', 'g', 'r', 'i', 'z_mag', 'I', 'J', 'H', 'K', 'W1', 'SPIT_5',
    'W2', 'SPIT_8', 'W3', 'W4', 'NUV', 'FUV')
names(ugriz) <- fields

sample_n(ugriz, 5)
attach(ugriz)

# Randomly split the dataset into training and testing subsets
set.seed(123) # for reproducible randomness in producing training and test sets
training.samples <- z %>% createDataPartition(p=0.5, list = FALSE)
train.data <- ugriz[training.samples]
test.data <- ugriz[-training.samples]

model <- train(z~., data = train.data, method = "knn",
    trControl = trainControl("cv", number = 10),
    preProcess = c("center","scale"),
    tuneLength = 10)

我的目标是根据列“u”、“g”、“r”、“i”、“z_mag”、“I”、“J”、“H”、“K”的幅度值来测试z的预测'，'W1'，'SPIT_5'，'W2'，'SPIT_8'，'W3'，'W4'，'NUV'，'FUV'，但我一直遇到错误

Error in terms.formula(formula, data = data) : 
  '.' in formula and no 'data' argument

如果我将公式更改为类似

model <- train(z~u, data = train.data, method = "knn",
    trControl = trainControl("cv", number = 10),
    preProcess = c("center","scale"),
    tuneLength = 10) # Gives error

我明白了

Error in eval(predvars, data, env) : 
  invalid 'envir' argument of type 'character'

我正在使用 RStudio v 1.3.959 和 R v 4.0.0 谷歌搜索错误给了我指向neuralnet网络中相同错误的链接，但caret中没有。 在这里，R 的某些早期版本中似乎存在错误。

是什么导致了错误？

Answer 1

您在数据分区中犯了一个错误。 在training.samples之后，您错过了一个“，”。 由于您没有提供任何数据，我正在使用iris数据

library(caret)
library(tidyverse)
# Randomly split the dataset into training and testing subsets
set.seed(123) # for reproducible randomness in producing training and test sets
training.samples <- createDataPartition(iris$Species ,p=0.5, list = FALSE)
train.data <- iris[training.samples,]
test.data <- iris[-training.samples, ]

train(Species~Sepal.Length, data = train.data, method = "knn",
      trControl = trainControl("cv", number = 10),
      preProcess = c("center","scale"),
      tuneLength = 10)

它没有给我任何错误。