For models that have only a single component, the tidy()
and
glance()
methods are identical. Please see the documentation for both
of those methods.
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.
tidy()
, stats::cor.test()
, stats::t.test()
,
stats::wilcox.test()
, stats::chisq.test()
Other htest tidiers:
augment.htest()
,
tidy.pairwise.htest()
,
tidy.power.htest()
A tibble::tibble()
with columns:
Alternative hypothesis (character).
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
Sometimes two estimates are computed, such as in a two-sample t-test.
Sometimes two estimates are computed, such as in a two-sample t-test.
Method used.
The two-sided p-value associated with the observed statistic.
The parameter being modeled.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
tt <- t.test(rnorm(10))
tidy(tt)
#> # A tibble: 1 × 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
# the glance output will be the same for each of the below tests
glance(tt)
#> # A tibble: 1 × 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 × 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 × 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 × 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 × 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 × 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