# Non-Standard Families of Tests

Source:`vignettes/articles/Non-Standard-Families-of-Tests.Rmd`

`Non-Standard-Families-of-Tests.Rmd`

At the moment, the `rwolf()`

function does not support
many different types of families of tests. E.g. if you had estimated the
following two regression models

\[ Y = \beta_0 + \beta_1 X_1 + \beta_2 X2 + u \] and

\[ Y = \beta_0 + \beta_3 X_3 + u \] and wanted to correct the following family of hypotheses for multiple testing:

\[ H_{0,A}: \beta_1 = 0 \text{ vs } H_{1,A}: \beta_1 \neq 0 \] \[ H_{0,B}: \beta_2 = 0 \text{ vs } H_{1,B}: \beta_2 \neq 0 \]

\[ H_{0,C}: \beta_1 + \beta_2 = 0 \text{ vs } H_{1,C}: \beta_1 + \beta_2\neq 0 \] \[ H_{0,D}: \beta_3 = 0 \text{ vs } H_{1,D}: \beta_3 \neq 0. \]

Unfortunately, the current API of `rwolf()`

does not yet
support such a family of tests. To apply the Romano Wolf corrections for
this family, you would have to follow three steps outlined below. But
first, let’s simulate some data.

```
N <- 1000
X1 <- rnorm(N)
X2 <- rnorm(N)
X3 <- rnorm(N)
Y <- 1 + 0.001 * X1 + 0.001 * X2 + 0.5 *X3 + rnorm(N)
cluster <- sample(1:20, N, TRUE)
df <- data.frame(Y = Y, X1 = X1, X2 = X2, X3 = X3, cluster)
```

### Step 2: Create bootstrapped t-statistics via
`fwildclusterboot::boottest()`

. Make sure to reset the random
seeds, so that all calls to `boottest()`

use the same
bootstrap weights

```
library(fwildclusterboot)
set.seed(123)
boot1 <- boottest(fit1, param = ~X1, B = 9999, clustid = ~cluster)
#> Warning: Please note that the seeding behavior for random number generation for
#> `boottest()` has changed with `fwildclusterboot` version 0.13.
#>
#> It will no longer be possible to exactly reproduce results produced by versions
#> lower than 0.13.
#>
#> If your prior results were produced under sufficiently many bootstrap
#> iterations, none of your conclusions will change. For more details about this
#> change, please read the notes in
#> [news.md](https://cran.r-project.org/web/packages/fwildclusterboot/news/news.html).
#> This warning is displayed once per session.
#> Too guarantee reproducibility, don't forget to set a global random seed
#> **both** via `set.seed()` and `dqrng::dqset.seed()`.
#> This message is displayed once every 8 hours.
set.seed(123)
boot2 <- boottest(fit1, param = ~X2, B = 9999, clustid = ~cluster)
set.seed(123)
boot3 <- boottest(fit1, param = ~X1 + X2, B = 9999, clustid = ~cluster)
set.seed(123)
boot4 <- boottest(fit2, param = ~ X3, B = 9999, clustid = ~cluster)
# get the bootstrapped t-stats from boottest
t_boot <- lapply(list(boot1, boot2, boot3, boot4),
function(x) x[["t_boot"]])
t_boot <- Reduce("cbind",t_boot)
# get the non-bootstrap t-stats from boottest
t_stat <- lapply(list(boot1, boot2, boot3, boot4),
function(x) teststat(x))
t_stat <- Reduce("cbind",t_stat)
```

### Step 3: feed the bootstrapped and non-bootstrapped t-statistics into
the `get_rwolf_pval()`

function

```
get_rwolf_pval(t_stats = t_stat, boot_t_stats = t_boot)
#> [1] 0.5454 0.4334 0.4334 0.0001
```

This returns a vector of multiple-testing adjusted pvalues for all hypotheses.