我尝试使用非参数自举来引导可靠性估计
我已经编写了下面的代码，其中创建了一个模型，然后引导了1000次以获得两个可靠性统计数据Alpha和Omega
我能够以置信区间visual =~ x1 + x2 + x3得到第一个构造的Alpha和Omega，但看不到其他构造textual和speed的访问方式
当我运行启动功能时，我会看到所有这些的结果
# bootstrapping with 1000 replications
results <- boot(data=data, statistic=reliability, R=500, formula=HS.model,parallel = 'snow')
> results$t0
visual textual speed total
alpha 0.6261171 0.8827069 0.6884550 0.7604886
omega 0.6253180 0.8851754 0.6877600 0.8453351
omega2 0.6253180 0.8851754 0.6877600 0.8453351
omega3 0.6120052 0.8850608 0.6858417 0.8596204
avevar 0.3705589 0.7210163 0.4244883 0.5145874
以下是我公认的劣质尝试。任何人都可以帮忙
library(lavaan)
library(semTools)
library(boot)
data <- HolzingerSwineford1939
HS.model <- 'visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
# function to reliability stats
reliability <- function(formula, data, indices) {
data = data
d <- data[indices,] # allows boot to select sample
fit <- cfa(HS.model, data=d)
semTools::reliability(fit)
}
# bootstrapping with 500 replications
results <- boot(data=data, statistic=reliability, R=500, formula=HS.model,parallel = 'snow')
# Get the confidence intervals
conf_interval_alpha <- boot.ci(results, type="bca", index = 1)
# Retrieve the Alpha and confidence intervals
alpha <- conf_interval_alpha$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
答案 0 :(得分：0)
首先，您需要查看使用完整数据集从reliability返回的内容：
> 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
然后，您需要查看从boot调用的内容：
> 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。