Tidy/glance a(n) htest object

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.

Value

A one-row tibble::tibble with one or more of the following columns, depending on which hypothesis test was used.

estimate

Estimate of the effect size

statistic

Test statistic used to compute the p-value

p.value

P-value

parameter

Parameter field in the htest, typically degrees of freedom

conf.low

Lower bound on a confidence interval

conf.high

Upper bound on a confidence interval

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 to compute the statistic as a string

alternative

Alternative hypothesis as a string

See also

tidy(), stats::cor.test(), stats::t.test(), stats::wilcox.test(), stats::chisq.test()

Other htest tidiers: augment.htest, tidy.pairwise.htest, tidy.power.htest

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 .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