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,
...
)

## Arguments

x 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 x that will serve as the response. This is alternative to using the formula argument. This is an alternative to the formula interface. The variable name in x that will serve as the explanatory variable. Optional. This is an alternative to the formula interface. 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 order = c("first", "second") means "first" - "second". Ignored for one-sample tests, and optional for two sample tests. Character string giving the direction of the alternative hypothesis. Options are "two-sided" (default), "greater", or "less". Only used when testing the null that a single proportion equals a given value, or that two proportions are equal; ignored otherwise. A logical value for whether to report the confidence interval or not. TRUE by default, ignored if p is specified for a two-sample test. Only used when testing the null that a single proportion equals a given value, or that two proportions are equal; ignored otherwise. 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 response that will be considered a success, as a string. Only used when testing the null that a single proportion equals a given value, or that two proportions are equal; ignored otherwise. A logical indicating whether Yates' continuity correction should be applied where possible. If z = TRUE, the correct argument will be overwritten as FALSE. Otherwise defaults to correct = TRUE. 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 correct = FALSE to see this connection. Additional arguments for prop.test().

## Examples

# 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