Aliases: tidy.htest htest_tidiers glance.htest
Keywords:
### ** 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 0.0441 0.134 0.897 9 -0.702 0.791 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 0.0441 0.134 0.897 9 -0.702 0.791 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