Tidy proportion test — prop

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.
formula	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.
response	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.
explanatory	The variable name in `x` that will serve as the explanatory variable. Optional. This is an alternative to the formula interface.
p	A numeric vector giving the hypothesized null proportion of success for each group.
order	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.
alternative	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.
conf_int	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.
conf_level	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.
success	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.
correct	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`.
z	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