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.

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()

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 × 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 × 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