R错误：承诺已在评估中

有人能解释一下如何修复这个错误吗？我意识到我没有提供所有的代码。这些数据来自著名的高尔夫天气数据。我得到的错误是probpnb中的错误（varClass=“play”，valueClass=I，inputVar1=“windy”，： 承诺已经在评估中：递归默认参数引用还是早期问题？我已经看到了这类问题的一些答案，但我并不完全理解它们，尤其是在本例中。尽管还有其他问题可以解决此错误，但不清楚此例中的错误来自何处

golf=read.csv(file="weather.nominal.csv")
df=as.data.frame(golf)
df$names<-rownames(df)


conditional_prob_bool <- function(spec11, spec12, spec21, spec22, df){ 

  consequent_bool <- df[,spec11] == spec12

  consequent_event <- df[consequent_bool,] 

  antecedent_bool <- df[,spec21] == spec22
  antecedent_event=df[antecedent_bool,]

  return(prob(intersect_event(consequent_event,antecedent_event,df),df)/prob(antecedent_event,df))
}



predNBVar2=function(varClass='play',inputVar1='windy',var1Level='TRUE',inputVar2='humidity',var2Level="high",df=df){
  ##probPCNB(varClass='play', valueClass='yes', inputVar1='windy',var1Level='TRUE',inputVar2='humidity',var2Level="high",df)
  #browser()
  #lev2=levels(df[,varClass])
  lev2=c('yes','no')
  #lev2=levels(df$varClass)
  countt=0
  classprob=0
  predictedclass=lev2[1]
  for(i in lev2){
    p=probPCNB(varClass='play', valueClass=i, inputVar1='windy',var1Level='TRUE',inputVar2='humidity',var2Level="high",df=df)
    if(classprob<p){
      classprob=p
      predictedclass=i
    }


  }
  return(paste("best",predictedclass," with measure",classprob ))

}




probPCNB=function(varClass='play', valueClass='true', inputVar1='windy',var1Level=TRUE,inputVar2='humidity',var2Level='high',df){
  #browser()

  A_bool=df[,inputVar1]==var1Level & df[,inputVar2]==var2Level
  A=df[A_bool,]

  class_bool=df[,varClass]==valueClass
  classE=df[class_bool,]


  numerator=prob(classE,df)*conditional_prob_bool(inputVar1, var1Level,varClass,valueClass,df)*conditional_prob_bool(inputVar2, var2Level,varClass,valueClass,df)


  lev=levels(df[,varClass])
  denominator=0

  for(i in lev){

    class_bool=df[,varClass]==i

    classE=df[class_bool,]
    class=i
    denominator= denominator+(prob(classE,df)*conditional_prob_bool(inputVar1, var1Level,varClass,class,df)*
                                conditional_prob_bool(inputVar2, var2Level,varClass,class,df))

  }
  return(numerator/denominator)

}

predNBVar2()

golf=read.csv（file=“weather.nominal.csv”）
df=as.data.frame（高尔夫）
df$names这是因为参数df=df

将df重命名为其他名称，它应该可以工作

另见：

golf=read.csv（file=“weather.nominal.csv”）
df=as.data.frame（高尔夫）
df$名称
golf=read.csv(file="weather.nominal.csv")
df=as.data.frame(golf)
df$names<-rownames(df)


conditional_prob_bool <- function(spec11, spec12, spec21, spec22, df){ 

  consequent_bool <- df[,spec11] == spec12

  consequent_event <- df[consequent_bool,] 

  antecedent_bool <- df[,spec21] == spec22
  antecedent_event=df[antecedent_bool,]

  return(prob(intersect_event(consequent_event,antecedent_event,df),df)/prob(antecedent_event,df))
}



predNBVar2=function(varClass='play',inputVar1='windy',var1Level='TRUE',inputVar2='humidity',var2Level="high",df_predNBVar2=df){
  ##probPCNB(varClass='play', valueClass='yes', inputVar1='windy',var1Level='TRUE',inputVar2='humidity',var2Level="high",df_predNBVar2)
  #browser()
  #lev2=levels(df[,varClass])
  lev2=c('yes','no')
  #lev2=levels(df$varClass)
  countt=0
  classprob=0
  predictedclass=lev2[1]
  for(i in lev2){
    p=probPCNB(varClass='play', valueClass=i, inputVar1='windy',var1Level='TRUE',inputVar2='humidity',var2Level="high",df=df_predNBVar2)
    if(classprob<p){
      classprob=p
      predictedclass=i
    }


  }
  return(paste("best",predictedclass," with measure",classprob ))

}




probPCNB=function(varClass='play', valueClass='true', inputVar1='windy',var1Level=TRUE,inputVar2='humidity',var2Level='high',df){
  #browser()

  A_bool=df[,inputVar1]==var1Level & df[,inputVar2]==var2Level
  A=df[A_bool,]

  class_bool=df[,varClass]==valueClass
  classE=df[class_bool,]


  numerator=prob(classE,df)*conditional_prob_bool(inputVar1, var1Level,varClass,valueClass,df)*conditional_prob_bool(inputVar2, var2Level,varClass,valueClass,df)


  lev=levels(df[,varClass])
  denominator=0

  for(i in lev){

    class_bool=df[,varClass]==i

    classE=df[class_bool,]
    class=i
    denominator= denominator+(prob(classE,df)*conditional_prob_bool(inputVar1, var1Level,varClass,class,df)*
                                conditional_prob_bool(inputVar2, var2Level,varClass,class,df))

  }
  return(numerator/denominator)

}

predNBVar2()