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.

Usage

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

# S3 method for class '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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

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          
#>      <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>           
#> 1   -0.218    -0.533   0.607         9    -1.14     0.706 One Sample t-te…
#> # ℹ 1 more variable: alternative <chr>

# 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          
#>      <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>           
#> 1   -0.218    -0.533   0.607         9    -1.14     0.706 One Sample t-te…
#> # ℹ 1 more variable: alternative <chr>

tt <- t.test(mpg ~ am, data = mtcars)

tidy(tt)
#> # A tibble: 1 × 10
#>   estimate estimate1 estimate2 statistic p.value parameter conf.low
#>      <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>
#> 1    -7.24      17.1      24.4     -3.77 0.00137      18.3    -11.3
#> # ℹ 3 more variables: conf.high <dbl>, 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… 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         
#>      <dbl>     <dbl>    <dbl>     <int>    <dbl>     <dbl> <chr>          
#> 1   -0.868     -9.56 1.29e-10        30   -0.934    -0.744 Pearson's prod…
#> # ℹ 1 more variable: alternative <chr>

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
#>   <fct>  <fct>     <dbl>  <dbl>     <dbl>     <dbl>     <dbl>  <dbl>
#> 1 Male   1st         180 0.0818    0.104     0.554      256.   -4.73
#> 2 Female 1st         145 0.0659    0.309     0.446       69.4   9.07
#> 3 Male   2nd         179 0.0813    0.103     0.628      224.   -3.02
#> 4 Female 2nd         106 0.0482    0.226     0.372       60.9   5.79
#> 5 Male   3rd         510 0.232     0.295     0.722      555.   -1.92
#> 6 Female 3rd         196 0.0891    0.417     0.278      151.    3.68
#> 7 Male   Crew        862 0.392     0.498     0.974      696.    6.29
#> 8 Female Crew         23 0.0104    0.0489    0.0260     189.  -12.1 
#> # ℹ 1 more variable: .std.resid <dbl>

Usage

Arguments

See also

Value

Examples