R中的Bootstrap t测试不依赖于MKexplore包

Question

我想在R中使用bootstrap获得配对t检验的CI。不幸的是，我没有安装MKinfer的权限。使用MKinfer，我想(这里是：在R中进行引导的T-测试(：

boot.t.test(
x = iris["Petal.Length"],
y = iris["Sepal.Length"],
alternative = c("two.sided"),
mu = 0,
#paired = TRUE,
conf.level = 0.95,
R = 9999
) 

对于CI和p值不依赖MKinfer(依赖boot也可以(的配对数据，我该如何做到这一点？

Answer 1

下面是一个使用boot使用R = 1000引导复制的示例

library(boot)
x <- iris$Petal.Length
y <- iris$Sepal.Length
change_in_mean <- function(df, indices) t.test(
df[indices, 1], df[indices, 2], paired = TRUE, var.equal = FALSE)$estimate
model <- boot(
data = cbind(x, y),
statistic = change_in_mean,
R = 1000)

我们可以使用boot.ci计算平均值估计变化的置信区间

boot.ci(model, type = "norm")
#BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
#Based on 1000 bootstrap replicates
#
#CALL :
#boot.ci(boot.out = model, type = "norm")
#
#Intervals :
#Level      Normal
#95%   (-2.262, -1.905 )
#Calculations and Intervals on Original Scale

请注意，这与t.test报告的CI非常接近

t.test(x, y, paired = TRUE, var.equal = FALSE)
#
#   Paired t-test
#
#data:  x and y
#t = -22.813, df = 149, p-value < 2.2e-16
#alternative hypothesis: true difference in means is not equal to 0
#95 percent confidence interval:
# -2.265959 -1.904708
#sample estimates:
#mean of the differences
#              -2.085333