使用 R 引导获取置信区间

Question

我正在尝试使用非参数引导程序引导可靠性估计 我已经编写了下面的代码，其中创建了一个模型，然后引导 1000 次以获得两个可靠性统计 Alpha 和 Omega 我能够获得具有置信区间的第一个构造的 Alpha 和 Omega：visual =~ x1 + x2 + x3 但看不到其他构造 textual 和 speed 的访问方法 当我运行引导功能时，我会看到所有结果

# bootstrapping with 1000 replications
results <- boot(data=data, statistic=reliability, R=500, formula=HS.model,parallel = 'snow')

> results$t0

        visual   textual     speed     total
alpha  0.6261171 0.8827069 0.6884550 0.7604886
omega  0.6253180 0.8851754 0.6877600 0.8453351
omega2 0.6253180 0.8851754 0.6877600 0.8453351
omega3 0.6120052 0.8850608 0.6858417 0.8596204
avevar 0.3705589 0.7210163 0.4244883 0.5145874

以下是我公认的粗制滥造的尝试。谁能帮忙

library(lavaan)
library(semTools)
library(boot)

data <- HolzingerSwineford1939

HS.model <- 'visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '

# function to reliability stats
reliability <- function(formula, data, indices) {
  data = data
  d <- data[indices,] # allows boot to select sample
  fit <- cfa(HS.model, data=d)
  semTools::reliability(fit)
}

# bootstrapping with 500 replications
results <- boot(data=data, statistic=reliability, R=500, formula=HS.model,parallel = 'snow')

# Get the confidence intervals
conf_interval_alpha <- boot.ci(results, type="bca", index = 1)

# Retrieve the Alpha and confidence intervals
alpha <- conf_interval_alpha$t0
alpha.ci <- conf_interval_alpha$bca[,c(4,5)]

# Retrieve the Omega and confidence intervals  
conf_interval_omega <- boot.ci(results, type="bca", index = 2)
omega <- conf_interval_omega$t0
omega.ci <- conf_interval_omega$bca[,c(4,5)]

感谢您的帮助

Answer 1

您首先需要查看从 reliability 返回的完整数据集：

> reliability(data=data)
          visual   textual     speed     total
alpha  0.6261171 0.8827069 0.6884550 0.7604886
omega  0.6253180 0.8851754 0.6877600 0.8453351
omega2 0.6253180 0.8851754 0.6877600 0.8453351
omega3 0.6120052 0.8850608 0.6858417 0.8596204
avevar 0.3705589 0.7210163 0.4244883 0.5145874

那么你需要看看你的boot-call返回了什么：

> str(results)
List of 11
 $ t0       : num [1:5, 1:4] 0.626 0.625 0.625 0.612 0.371 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:5] "alpha" "omega" "omega2" "omega3" ...
  .. ..$ : chr [1:4] "visual" "textual" "speed" "total"
 $ t        : num [1:500, 1:20] 0.594 0.607 0.613 0.669 0.621 ...
 $ R        : num 500
 $ data     :'data.frame':  301 obs. of  15 variables:
  ..$ id    : int [1:301] 1 2 3 4 5 6 7 8 9 11 ...
  ..$ sex   : int [1:301] 1 2 2 1 2 2 1 2 2 2 ...
  ..$ ageyr : int [1:301] 13 13 13 13 12 14 12 12 13 12 ...
  ..$ agemo : int [1:301] 1 7 1 2 2 1 1 2 0 5 ...
  ..$ school: Factor w/ 2 levels "Grant-White",..: 2 2 2 2 2 2 2 2 2 2 ...
  ..$ grade : int [1:301] 7 7 7 7 7 7 7 7 7 7 ...
  ..$ x1    : num [1:301] 3.33 5.33 4.5 5.33 4.83 ...
  ..$ x2    : num [1:301] 7.75 5.25 5.25 7.75 4.75 5 6 6.25 5.75 5.25 ...
  ..$ x3    : num [1:301] 0.375 2.125 1.875 3 0.875 ...
  ..$ x4    : num [1:301] 2.33 1.67 1 2.67 2.67 ...
  ..$ x5    : num [1:301] 5.75 3 1.75 4.5 4 3 6 4.25 5.75 5 ...
  ..$ x6    : num [1:301] 1.286 1.286 0.429 2.429 2.571 ...
  ..$ x7    : num [1:301] 3.39 3.78 3.26 3 3.7 ...
  ..$ x8    : num [1:301] 5.75 6.25 3.9 5.3 6.3 6.65 6.2 5.15 4.65 4.55 ...
  ..$ x9    : num [1:301] 6.36 7.92 4.42 4.86 5.92 ...
 $ seed     : int [1:626] 403 330 1346657232 1136157038 -874329217 857221657 1850455833 952027245 2020402269 -1198488986 ...
 $ statistic:function (formula, data, indices)  
  ..- attr(*, "srcref")=Class 'srcref'  atomic [1:8] 1 16 6 1 16 1 1 6
  .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x7fa57918d430> 
 $ sim      : chr "ordinary"
 $ call     : language boot(data = data, statistic = reliability, R = 500, formula = HS.model, parallel = "snow")
 $ stype    : chr "i"
 $ strata   : num [1:301] 1 1 1 1 1 1 1 1 1 1 ...
 $ weights  : num [1:301] 0.00332 0.00332 0.00332 0.00332 0.00332 ...
 - attr(*, "class")= chr "boot"

....所以results$t0 包含所有三个模型参数估计值。