For models that have only a single component, the tidy() and glance() methods are identical. Please see the documentation for both of those methods.

# S3 method for htest
tidy(x, ...)

# S3 method for htest
glance(x, ...)

Arguments

x

An htest objected, such as those created by stats::cor.test(), stats::t.test(), stats::wilcox.test(), stats::chisq.test(), etc.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

Value

A tibble::tibble() with columns:

alternative

Alternative hypothesis (character).

conf.high

The upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

conf.low

The lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

estimate

The estimated value of the regression term.

estimate1

Sometimes two estimates are computed, such as in a two-sample t-test

estimate2

Sometimes two estimates are computed, such as in a two-sample t-test

method

Needs custom info.

p.value

The two-sided p-value associated with the observed statistic.

parameter

Needs custom info.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

Examples

tt <- t.test(rnorm(10)) tidy(tt)
#> # A tibble: 1 x 8 #> estimate statistic p.value parameter conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> #> 1 -0.218 -0.533 0.607 9 -1.14 0.706 One Samp… two.sided
glance(tt) # same output for all htests
#> # A tibble: 1 x 8 #> estimate statistic p.value parameter conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> #> 1 -0.218 -0.533 0.607 9 -1.14 0.706 One Samp… two.sided
tt <- t.test(mpg ~ am, data = mtcars) tidy(tt)
#> # A tibble: 1 x 10 #> estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 -7.24 17.1 24.4 -3.77 0.00137 18.3 -11.3 -3.21 #> # ... with 2 more variables: method <chr>, alternative <chr>
wt <- wilcox.test(mpg ~ am, data = mtcars, conf.int = TRUE, exact = FALSE) tidy(wt)
#> # A tibble: 1 x 7 #> estimate statistic p.value conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> #> 1 -6.80 42 0.00187 -11.7 -2.90 Wilcoxon rank sum … two.sided
ct <- cor.test(mtcars$wt, mtcars$mpg) tidy(ct)
#> # A tibble: 1 x 8 #> estimate statistic p.value parameter conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <chr> <chr> #> 1 -0.868 -9.56 1.29e-10 30 -0.934 -0.744 Pearson… two.sided
chit <- chisq.test(xtabs(Freq ~ Sex + Class, data = as.data.frame(Titanic))) tidy(chit)
#> # A tibble: 1 x 4 #> statistic p.value parameter method #> <dbl> <dbl> <int> <chr> #> 1 350. 1.56e-75 3 Pearson's Chi-squared test
augment(chit)
#> # A tibble: 8 x 9 #> Sex Class .observed .prop .row.prop .col.prop .expected .residuals .stdres #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Male 1st 180 0.0818 0.104 0.554 256. -4.73 -11.1 #> 2 Female 1st 145 0.0659 0.309 0.446 69.4 9.07 11.1 #> 3 Male 2nd 179 0.0813 0.103 0.628 224. -3.02 -6.99 #> 4 Female 2nd 106 0.0482 0.226 0.372 60.9 5.79 6.99 #> 5 Male 3rd 510 0.232 0.295 0.722 555. -1.92 -5.04 #> 6 Female 3rd 196 0.0891 0.417 0.278 151. 3.68 5.04 #> 7 Male Crew 862 0.392 0.498 0.974 696. 6.29 17.6 #> 8 Female Crew 23 0.0104 0.0489 0.0260 189. -12.1 -17.6