t-test (one-sample, two-sample, Welch, paired)

Performs t-tests on numeric columns of a tidy data frame. Each test returns one row; multiple columns and .by slices are handled by row repetition, with an optional p-value adjustment across rows.

Usage

stat_t_test(
  data,
  .cols,
  group,
  .by = NULL,
  mu = 0,
  paired = FALSE,
  var.equal = FALSE,
  alternative = "two.sided",
  conf_level = 0.95,
  p_adjust = "none"
)

Arguments

data

A data frame.

.cols

<tidy-select> Numeric test variable(s). One column for one-/two-sample tests; exactly two columns when paired = TRUE.

group

<tidy-select> Optional grouping column with exactly two levels (the two groups to compare). Required for two-sample tests, forbidden for paired and one-sample tests.

.by

<tidy-select> Optional slice columns; the test is repeated separately within each slice.

mu

Null value of the mean (or mean difference).

paired

Logical; paired test. Requires exactly two .cols and no group (differences are taken row-wise).

var.equal

Logical; pooled-variance t-test (FALSE gives Welch).

alternative

"two.sided", "greater", or "less".

conf_level

Confidence level.

p_adjust

P-value adjustment (see stats::p.adjust) applied across the returned rows.

Value

A tibble with class stat_infer: variable, .by identifiers, n_used, n_dropped, estimate, estimate1, estimate2, statistic, df, p.value, p.adjusted, sig, conf_low, conf_high, effect (Cohen's d), method, alternative. For two-sample tests all difference quantities (estimate, statistic, CI, effect) follow the group1 - group2 direction (first level minus second level, rstatix convention).

Examples

stat_t_test(sleep, .cols = extra, group = group)
#> t-test (two.sided) 
#> # A tibble: 1 × 16
#>   variable n_used n_dropped estimate estimate1 estimate2 statistic    df p.value
#> * <chr>     <int>     <int>    <dbl>     <dbl>     <dbl>     <dbl> <dbl>   <dbl>
#> 1 extra        20         0    -1.58      0.75      2.33     -1.86  17.8  0.0794
#> # ℹ 7 more variables: conf_low <dbl>, conf_high <dbl>, effect <dbl>,
#> #   method <chr>, alternative <chr>, p.adjusted <dbl>, sig <chr>
stat_t_test(mtcars, .cols = mpg, mu = 20)
#> t-test (two.sided) 
#> # A tibble: 1 × 16
#>   variable n_used n_dropped estimate estimate1 estimate2 statistic    df p.value
#> * <chr>     <int>     <int>    <dbl>     <dbl>     <dbl>     <dbl> <dbl>   <dbl>
#> 1 mpg          32         0     20.1        NA        NA    0.0851    31   0.933
#> # ℹ 7 more variables: conf_low <dbl>, conf_high <dbl>, effect <dbl>,
#> #   method <chr>, alternative <chr>, p.adjusted <dbl>, sig <chr>
stat_t_test(mtcars, .cols = mpg, group = am, var.equal = TRUE)
#> t-test (two.sided) 
#> # A tibble: 1 × 16
#>   variable n_used n_dropped estimate estimate1 estimate2 statistic    df p.value
#> * <chr>     <int>     <int>    <dbl>     <dbl>     <dbl>     <dbl> <dbl>   <dbl>
#> 1 mpg          32         0    -7.24      17.1      24.4     -4.11    30 2.85e-4
#> # ℹ 7 more variables: conf_low <dbl>, conf_high <dbl>, effect <dbl>,
#> #   method <chr>, alternative <chr>, p.adjusted <dbl>, sig <chr>