如何计算 R 中的 Bonferroni 下限和上限？

Question

使用以下数据，我正在尝试计算卡方和 Bonferroni 上下置信区间。 "Data_No" 列标识数据集（因为需要为每个数据集单独进行计算）。

Data_No    Area    Observed
   1        3353    31
   1        2297    2
   1        1590    15
   1        1087    16
   1        817     2
   1        847     10
   1        1014    28
   1        872     29
   1        1026    29
   1        1215    21
   2        3353    31
   2        2297    2
   2        1590    15
   3        1087    16
   3        817     2

我使用的代码是

        library(dplyr) 
        setwd("F:/GIS/July 2019/") 
        total_data <- read.csv("test.csv") 
        result_data <- NULL 
        for(i in unique(total_data$Data_No)){ 
        data <- total_data[which(total_data$Data_No == i),] data <- data %>%
        mutate(RelativeArea = Area/sum(Area), Expected = RelativeArea*sum(Observed), OminusE = Observed-Expected, O2 = OminusE^2, O2divE = O2/Expected, APU = Observed/sum(Observed), Alpha = 0.05/2*count(Data_No), 
Zvalue = qnorm(Alpha,lower.tail=FALSE), lower = APU-Zvalue*sqrt(APU*(1-APU)/sum(Observed)), upper = APU+Zvalue*sqrt(APU*(1-APU)/sum(Observed)))
result_data <- rbind(result_data,data) }
write.csv(result_data,file='final_result.csv')

我收到的错误信息是：

Error in UseMethod("summarise_") : no applicable method for 'summarise_' applied to an object of class "c('integer', 'numeric')"

我调用的列 "Alpha" 是 0.05/2k 的 alpha 值，其中 K 是类别数 - 在我的示例中，我有 10 个类别（"Data_No" 列）第一个数据集，所以"Alpha"需要0.05/20 = 0.0025，对应的Z值为2.807。在我的示例中，第二个数据集有 3 个类别（因此 0.05/6），第三个数据集有 2 个类别（0.05/4）table（Data_No" 列）。使用新计算的值 "Alpha" 列，然后我需要计算 ZValue 列 (Zvalue = qnorm(Alpha,lower.tail=FALSE))，然后我用它来计算上下置信区间。

根据以上数据，这是我应该得到的结果，但请注意，我不得不手动计算 Alpha 列和 Z 值，而不是将这些计算插入 R 代码中：

Data_No Area    Observed    RelativeArea    Alpha   Z value lower   upper
    1   3353    31          0.237           0.003   2.807   0.092   0.247
    1   2297    2           0.163           0.003   2.807   -0.011  0.033
    1   1590    15          0.113           0.003   2.807   0.025   0.139
    1   1087    16          0.077           0.003   2.807   0.029   0.146
    1   817     2           0.058           0.003   2.807   -0.011  0.033
    1   847     10          0.060           0.003   2.807   0.007   0.102
    1   1014    28          0.072           0.003   2.807   0.078   0.228
    1   872     29          0.062           0.003   2.807   0.083   0.234
    1   1026    29          0.073           0.003   2.807   0.083   0.234
    1   1215    21          0.086           0.003   2.807   0.049   0.181
    2   3353    31          0.463           0.008   2.394   0.481   0.811
    2   2297    2           0.317           0.008   2.394   -0.027  0.111
    2   1590    15          0.220           0.008   2.394   0.152   0.473
    3   1087    16          0.571           0.013   2.241   0.723   1.055
    3   817     2           0.429           0.013   2.241   -0.055  0.277

请注意，我只包含了一些从代码生成的列。

Answer 1

# You need to check the closing bracket for lower c.f. sqrt value. Following code should work.

data <- read.csv("test.csv") 
data <- data %>% mutate(RelativeArea =
                          Area/sum(Area), Expected = RelativeArea*sum(Observed), OminusE =
                          Observed-Expected, O2 = OminusE^2, O2divE = O2/Expected, APU =
                          Observed/sum(Observed), lower =
                          APU-2.394*sqrt(APU*(1-APU)/sum(Observed)), upper =
                                           APU+2.394*sqrt(APU*(1-APU)/sum(Observed)))



#Answer to follow-up question.
#Sample Data
Data_No   Area   Observed
1         3353    31
1         2297    2
2         1590    15
2         1087    16

#Code to run
total_data <- read.csv("test.csv")
result_data <- NULL
for(i in unique(total_data$Data_No)){
data <- total_data[which(total_data$Data_No == i),]
data <- data %>% mutate(RelativeArea =
                          Area/sum(Area), Expected = RelativeArea*sum(Observed), OminusE =
                          Observed-Expected, O2 = OminusE^2, O2divE = O2/Expected, APU =
                          Observed/sum(Observed), lower =
                          APU-2.394*sqrt(APU*(1-APU)/sum(Observed)), upper =
                                           APU+2.394*sqrt(APU*(1-APU)/sum(Observed)))

result_data <- rbind(result_data,data)
}

write.csv(result_data,file='final_result.csv')

Answer 2

       #Issue in calculating Alpha. I have updated the code.    
       library(dplyr) 
       setwd("F:/GIS/July 2019/") 
       total_data <- read.csv("test.csv") 
       #Creating the NO_OF_CATEGORIES column based on your question.
       total_data$NO_OF_CATEGORIES <- 0
       total_data[which(total_data$Data_No==1),]$NO_OF_CATEGORIES <- 10
       total_data[which(total_data$Data_No==2),]$NO_OF_CATEGORIES <- 3
       total_data[which(total_data$Data_No==3),]$NO_OF_CATEGORIES <- 2


       #Actual code


     result_data <- NULL 

for(i in unique(total_data$Data_No)){ 
  data <- total_data[which(total_data$Data_No == i),] 
  data <- data %>%
    mutate(RelativeArea = Area/sum(Area), Expected = RelativeArea*sum(Observed), OminusE = Observed-Expected, O2 = OminusE^2, O2divE = O2/Expected, APU = Observed/sum(Observed), Alpha = 0.05/(2*(unique(data$NO_OF_CATEGORIES))), 
           Zvalue = qnorm(Alpha,lower.tail=FALSE), lower = APU-Zvalue*sqrt(APU*(1-APU)/sum(Observed)), upper = APU+Zvalue*sqrt(APU*(1-APU)/sum(Observed)))
  result_data <- rbind(result_data,data) }
write.csv(result_data,file='final_result.csv')

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