Hi @Rsky,
This is probably how I'd do it:
library(rsample)
library(tibble)
library(purrr)
group_1 <- rnorm(20,250,1)
group_2 <- rnorm(20,260,1)
dd <- tibble(
gp = c(rep(1, 20), rep(2, 20)),
x = c(group_1, group_2)
)
boots <- bootstraps(dd, times = 100)
results <- map(boots$splits, ~t.test(x ~ gp, data = analysis(.x)))
results
#> [[1]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -39.723, df = 37.979, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.387307 -9.379903
#> sample estimates:
#> mean in group 1 mean in group 2
#> 249.9325 259.8161
#>
#>
#> [[2]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -39.95, df = 32.403, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.636565 -9.605016
#> sample estimates:
#> mean in group 1 mean in group 2
#> 249.8622 259.9830
#>
#>
#> [[3]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -38.852, df = 30.97, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.496965 -9.449833
#> sample estimates:
#> mean in group 1 mean in group 2
#> 249.9768 259.9502
#>
#>
#> [[4]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -36.184, df = 37.634, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.140577 -9.065691
#> sample estimates:
#> mean in group 1 mean in group 2
#> 250.1526 259.7558
#>
#>
#> [[5]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -36.379, df = 37.399, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -9.570911 -8.561376
#> sample estimates:
#> mean in group 1 mean in group 2
#> 250.5136 259.5797
#>
#>
#> [[6]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -34.786, df = 37.945, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.246793 -9.119708
#> sample estimates:
#> mean in group 1 mean in group 2
#> 250.2552 259.9385
#>
#>
#> [[7]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -45.541, df = 33.333, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.154724 -9.286519
#> sample estimates:
#> mean in group 1 mean in group 2
#> 250.0092 259.7298
#>
#>
#> [[8]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -48.884, df = 33.506, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.502344 -9.663542
#> sample estimates:
#> mean in group 1 mean in group 2
#> 249.7921 259.8750
#>
#>
#> [[9]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -35.212, df = 30.62, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.171712 -9.057381
#> sample estimates:
#> mean in group 1 mean in group 2
#> 250.2398 259.8543
#>
#>
#> [[10]]
#>
#> Welch Two Sample t-test
#>
#> data: x by gp
#> t = -36.841, df = 33.874, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -10.197028 -9.130719
#> sample estimates:
#> mean in group 1 mean in group 2
#> 250.0272 259.6911
#>
#>
#> [ reached getOption("max.print") -- omitted 90 entries ]
results
is a list of t.test
objects that has as many list-entries as number of bootstrap replicates. results
can be inspected in whatever way you desire.