在R中进行逻辑回归时出错：对比仅适用于具有2个或更多水平的因素

我在建立逻辑回归模型时遇到了这个错误：

对比度

中的错误
library("dplyr")
library("caret")
bank_eval <- read.csv("bank_eval.csv",stringsAsFactors = FALSE) 
head(bank_eval)


anyNA(bank_eval)
summary(bank_eval)
str(bank_eval)




#replace unknown val with NA
bank_eval <- bank_eval %>%
  mutate(job = replace(job, job == "unknown", NA),
         marital = replace(marital, marital == "unknown", NA),
         education = replace(education, education == "unknown", NA),
         default = replace(default, default == "unknown", NA),
         housing = replace(housing, housing == "unknown", NA),
         loan = replace(loan, loan == "unknown", NA),
         housing = replace(housing, housing == "unknown", NA)
         )
bank_eval  

summary(bank_eval)
str(bank_eval)


#replacing NA Categorical values with frequent val

bank_eval <- bank_eval %>%
  
  mutate(job= replace(job,
                         is.na(job),
                         "admin"),
         marital= replace(marital,
                      is.na(marital),
                      "married"),
         education= replace(education,
                          is.na(education),
                          "university.degree"),
         default= replace(default,
                            is.na(default),
                            "no"),
         housing= replace(housing,
                            is.na(housing),
                            "yes"),
         loan= replace(loan,
                            is.na(loan),
                            "no")
         )
bank_eval
anyNA(bank_eval)
summary(bank_eval)


bank_eval <- bank_eval %>%
  mutate_if(is.character, as.factor)
bank_eval
str(bank_eval)
summary(bank_eval)

set.seed(123)
Trainingindex =createDataPartition(y= bank_eval$y, p=0.8, list=FALSE)
trainset = bank_eval[Trainingindex, ] #80% data for training
testset = bank_eval[-Trainingindex, ]
dim(trainset)
dim(testset)
#building and Training model using training set
sapply(trainset, function(x) if (is.factor(x)) length(levels(x)) else NA)
LGModel<-glm(y~., data = trainset, 
             family = binomial(logit))
summary(LGModel)

$ age           : int  30 39 25 38 47 32 32 41 31 35 ...
 $ job           : Factor w/ 12 levels "admin","admin.",..: 3 9 9 9 2 9 2 4 9 3 ...
 $ marital       : Factor w/ 3 levels "divorced","married",..: 2 3 2 2 2 3 3 2 1 2 ...
 $ education     : Factor w/ 7 levels "basic.4y","basic.6y",..: 3 4 4 3 7 7 7 7 6 3 ...
 $ default       : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
 $ housing       : Factor w/ 2 levels "no","yes": 2 1 2 2 2 1 2 2 1 1 ...
 $ loan          : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
 $ contact       : Factor w/ 2 levels "cellular","telephone": 1 2 2 2 1 1 1 1 1 2 ...
 $ month         : Factor w/ 10 levels "apr","aug","dec",..: 7 7 5 5 8 10 10 8 8 7 ...
 $ day_of_week   : Factor w/ 5 levels "fri","mon","thu",..: 1 1 5 1 2 3 2 2 4 3 ...
 $ duration      : int  487 346 227 17 58 128 290 44 68 170 ...
 $ campaign      : int  2 4 1 3 1 3 4 2 1 1 ...
 $ pdays         : int  999 999 999 999 999 999 999 999 999 999 ...
 $ previous      : int  0 0 0 0 0 2 0 0 1 0 ...
 $ poutcome      : Factor w/ 3 levels "failure","nonexistent",..: 2 2 2 2 2 1 2 2 1 2 ...
 $ emp.var.rate  : num  -1.8 1.1 1.4 1.4 -0.1 -1.1 -1.1 -0.1 -0.1 1.1 ...
 $ cons.price.idx: num  92.9 94 94.5 94.5 93.2 ...
 $ cons.conf.idx : num  -46.2 -36.4 -41.8 -41.8 -42 -37.5 -37.5 -42 -42 -36.4 ...
 $ euribor3m     : num  1.31 4.86 4.96 4.96 4.19 ...
 $ nr.employed   : num  5099 5191 5228 5228 5196 ...
 $ y             : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...