R-插入符号序列（）；错误：停止"；加上；在newdata中未找到对象中使用的所有变量名；

我正在尝试建立一个简单的。我想使用所有变量作为分类预测因子来预测蘑菇是否可以食用

我正在使用软件包

以下是我的完整代码：

##################################################################################
# Prepare R and R Studio environment
##################################################################################

# Clear the R studio console
cat("\014")

# Remove objects from environment
rm(list = ls())

# Install and load packages if necessary
if (!require(tidyverse)) {
  install.packages("tidyverse")
  library(tidyverse)
}
if (!require(caret)) {
  install.packages("caret")
  library(caret)
}
if (!require(klaR)) {
  install.packages("klaR")
  library(klaR)
}

#################################

mushrooms <- read.csv("agaricus-lepiota.data", stringsAsFactors = TRUE, header = FALSE)

na.omit(mushrooms)

names(mushrooms) <- c("edibility", "capShape", "capSurface", "cap-color", "bruises", "odor", "gill-attachment", "gill-spacing", "gill-size", "gill-color", "stalk-shape", "stalk-root", "stalk-surface-above-ring", "stalk-surface-below-ring", "stalk-color-above-ring", "stalk-color-below-ring", "veil-type", "veil-color", "ring-number", "ring-type", "spore-print-color", "population", "habitat")

# convert bruises to a logical variable
mushrooms$bruises <- mushrooms$bruises == 't'

set.seed(1234)
split <- createDataPartition(mushrooms$edibility, p = 0.8, list = FALSE)

train <- mushrooms[split, ]
test <- mushrooms[-split, ]

predictors <- names(train)[2:20] #Create response and predictor data

x <- train[,predictors] #predictors
y <- train$edibility #response

train_control <- trainControl(method = "cv", number = 1) # Set up 1 fold cross validation

edibility_mod1 <- train( #train the model
  x = x,
  y = y,
  method = "nb", 
  trControl = train_control
)

脚本运行后的x和y：

> str(x)
'data.frame':   6500 obs. of  19 variables:
 $ capShape                : Factor w/ 6 levels "b","c","f","k",..: 6 6 1 6 6 6 1 1 6 1 ...
 $ capSurface              : Factor w/ 4 levels "f","g","s","y": 3 3 3 4 3 4 3 4 4 3 ...
 $ cap-color               : Factor w/ 10 levels "b","c","e","g",..: 5 10 9 9 4 10 9 9 9 10 ...
 $ bruises                 : logi  TRUE TRUE TRUE TRUE FALSE TRUE ...
 $ odor                    : Factor w/ 9 levels "a","c","f","l",..: 7 1 4 7 6 1 1 4 7 1 ...
 $ gill-attachment         : Factor w/ 2 levels "a","f": 2 2 2 2 2 2 2 2 2 2 ...
 $ gill-spacing            : Factor w/ 2 levels "c","w": 1 1 1 1 2 1 1 1 1 1 ...
 $ gill-size               : Factor w/ 2 levels "b","n": 2 1 1 2 1 1 1 1 2 1 ...
 $ gill-color              : Factor w/ 12 levels "b","e","g","h",..: 5 5 6 6 5 6 3 6 8 3 ...
 $ stalk-shape             : Factor w/ 2 levels "e","t": 1 1 1 1 2 1 1 1 1 1 ...
 $ stalk-root              : Factor w/ 5 levels "?","b","c","e",..: 4 3 3 4 4 3 3 3 4 3 ...
 $ stalk-surface-above-ring: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
 $ stalk-surface-below-ring: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
 $ stalk-color-above-ring  : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
 $ stalk-color-below-ring  : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
 $ veil-type               : Factor w/ 1 level "p": 1 1 1 1 1 1 1 1 1 1 ...
 $ veil-color              : Factor w/ 4 levels "n","o","w","y": 3 3 3 3 3 3 3 3 3 3 ...
 $ ring-number             : Factor w/ 3 levels "n","o","t": 2 2 2 2 2 2 2 2 2 2 ...
 $ ring-type               : Factor w/ 5 levels "e","f","l","n",..: 5 5 5 5 1 5 5 5 5 5 ...



> str(y)
 Factor w/ 2 levels "e","p": 2 1 1 2 1 1 1 1 2 1 ...

我的环境是：

> R.version
               _                           
platform       x86_64-apple-darwin17.0     
arch           x86_64                      
os             darwin17.0                  
system         x86_64, darwin17.0          
status                                     
major          4                           
minor          0.3                         
year           2020                        
month          10                          
day            10                          
svn rev        79318                       
language       R                           
version.string R version 4.0.3 (2020-10-10)
nickname       Bunny-Wunnies Freak Out     
> RStudio.Version()
$citation

To cite RStudio in publications use:

  RStudio Team (2020). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {RStudio: Integrated Development Environment for R},
    author = {{RStudio Team}},
    organization = {RStudio, PBC},
    address = {Boston, MA},
    year = {2020},
    url = {http://www.rstudio.com/},
  }


$mode
[1] "desktop"

$version
[1] ‘1.3.1093’

$release_name
[1] "Apricot Nasturtium"

---

您试图做的是一个有点棘手的、最简单的bayes实现，或者至少您正在使用的一个（从e1071派生的kLAR）使用正态分布。您可以在下面的详细信息中看到：

标准朴素贝叶斯分类器（至少是这个实现） 假设预测变量和高斯分布独立 度量预测器的分布（给定目标类）。对于 如果属性缺少值，则对应的表项为 为预测而省略

你的预测是绝对的，所以这可能是有问题的。您可以尝试设置

kernel=TRUE

和

adjust=1

以强制其恢复正常，并避免引发错误的

kernel=FALSE

在此之前，我们删除只有1个级别的列并对列名进行排序，在这种情况下，使用公式和避免生成伪变量更容易：

df = train 
levels(df[["veil-type"]])
[1] "p"
df[["veil-type"]]=NULL
colnames(df) = gsub("-","_",colnames(df))

Grid = expand.grid(usekernel=TRUE,adjust=1,fL=c(0.2,0.5,0.8))

mod1 <- train(edibility~.,data=df,
  method = "nb", trControl = trainControl(method="cv",number=5),
  tuneGrid=Grid
)

 mod1
Naive Bayes 

6500 samples
  21 predictor
   2 classes: 'e', 'p' 

No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 5200, 5200, 5200, 5200, 5200 
Resampling results across tuning parameters:

  fL   Accuracy   Kappa    
  0.2  0.9243077  0.8478624
  0.5  0.9243077  0.8478624
  0.8  0.9243077  0.8478624

Tuning parameter 'usekernel' was held constant at a value of TRUE

Tuning parameter 'adjust' was held constant at a value of 1
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were fL = 0.2, usekernel = TRUE and
 adjust = 1.

df=列车
级别（df[[“面纱类型”]]
[1] “p”
df[[“面纱类型”]]=NULL
colnames（df）=gsub（“-”，“34;”，colnames（df））
Grid=expand.Grid（usekernel=TRUE，adjust=1，fL=c（0.2,0.5,0.8））
mod1可能是目标变量的类不平衡的问题：也不确定目标变量是否需要因子？你们正在阅读它，就像它看起来的文本一样……我不会因为类不平衡而得到一个更明确的错误。不管怎样，我都会研究它。y是因子，用输出更新问题以显示是否有用。编辑问题中的x和y输出显示除一个逻辑变量外，所有x变量都是因子。我将检查NA，好主意。如果我的预测是非度量的，即分类/标称/因子，为什么NB算法需要使用高斯分布或非参数核技术。我是新来的，所以请让我知道我错过了什么。我现在正尝试使用多项式的_naive _bayes（）函数，我认为它可能更适合我，但我不知道如何进行后处理，请看这里的问题：模型需要评估给定预测值的观测的条件概率，并且大多数假设你的预测值是高斯的。你可以看到。在本博客的其余部分，它解释了互惠互利的运作方式
df = train 
levels(df[["veil-type"]])
[1] "p"
df[["veil-type"]]=NULL
colnames(df) = gsub("-","_",colnames(df))

Grid = expand.grid(usekernel=TRUE,adjust=1,fL=c(0.2,0.5,0.8))

mod1 <- train(edibility~.,data=df,
  method = "nb", trControl = trainControl(method="cv",number=5),
  tuneGrid=Grid
)

 mod1
Naive Bayes 

6500 samples
  21 predictor
   2 classes: 'e', 'p' 

No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 5200, 5200, 5200, 5200, 5200 
Resampling results across tuning parameters:

  fL   Accuracy   Kappa    
  0.2  0.9243077  0.8478624
  0.5  0.9243077  0.8478624
  0.8  0.9243077  0.8478624

Tuning parameter 'usekernel' was held constant at a value of TRUE

Tuning parameter 'adjust' was held constant at a value of 1
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were fL = 0.2, usekernel = TRUE and
 adjust = 1.