A tidier version of prop.test() for equal or given proportions.
prop_test( x, formula, response = NULL, explanatory = NULL, p = NULL, order = NULL, alternative = "two-sided", conf_int = TRUE, conf_level = 0.95, success = NULL, correct = NULL, z = FALSE, ... )
A data frame that can be coerced into a tibble.
A formula with the response variable on the left and the explanatory on the right, where an explanatory variable NULL indicates a test of a single proportion.
The variable name in
The variable name in
A numeric vector giving the hypothesized null proportion of success for each group.
A string vector specifying the order in which the proportions
should be subtracted, where
Character string giving the direction of the alternative
hypothesis. Options are
A logical value for whether to report the confidence
interval or not.
A numeric value between 0 and 1. Default value is 0.95. Only used when testing the null that a single proportion equals a given value, or that two proportions are equal; ignored otherwise.
The level of
A logical indicating whether Yates' continuity correction
should be applied where possible. If
A logical value for whether to report the statistic as a standard
normal deviate or a Pearson's chi-square statistic. \(z^2\) is distributed
chi-square with 1 degree of freedom, though note that the user will likely
need to turn off Yates' continuity correction by setting
Additional arguments for prop.test().
# two-sample proportion test for difference in proportions of # college completion by respondent sex prop_test(gss, college ~ sex, order = c("female", "male"))#> # A tibble: 1 x 6 #> statistic chisq_df p_value alternative lower_ci upper_ci #> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> #> 1 0.0000204 1 0.996 two.sided -0.101 0.0917# one-sample proportion test for hypothesized null # proportion of college completion of .2 prop_test(gss, college ~ NULL, p = .2)#> # A tibble: 1 x 4 #> statistic chisq_df p_value alternative #> <dbl> <int> <dbl> <chr> #> 1 636. 1 2.98e-140 two.sided# report as a z-statistic rather than chi-square # and specify the success level of the response prop_test(gss, college ~ NULL, success = "degree", p = .2, z = TRUE)#> # A tibble: 1 x 3 #> statistic p_value alternative #> <dbl> <dbl> <chr> #> 1 8.27 1.30e-16 two.sided