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

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

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

Method used.

p.value

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

parameter

The parameter being modeled.

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 Sampl… 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 Sampl… 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 t… 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 .resid .std.resid #> <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