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Tidy proportion test

Source: R/wrappers.R
prop_test.Rd

A tidier version of prop.test() for equal or given proportions.

Usage

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. Alternatively, a response and explanatory argument can be supplied.

response

The variable name in x that will serve as the response. This is an alternative to using the formula argument.

explanatory

The variable name in x that will serve as the explanatory variable. This is an alternative to using the formula argument.

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 include the confidence interval or not. TRUE by default.

conf_level

A numeric value between 0 and 1. Default value is 0.95.

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().

Details

When testing with an explanatory variable with more than two levels, the order argument as used in the package is no longer well-defined. The function will thus raise a warning and ignore the value if supplied a non-NULL order argument.

The columns present in the output depend on the output of both prop.test() and broom::glance.htest(). See the latter's documentation for column definitions; columns have been renamed with the following mapping:

  • chisq_df = parameter

  • p_value = p.value

  • lower_ci = conf.low

  • upper_ci = conf.high

See also

Other wrapper functions: chisq_stat(), chisq_test(), observe(), t_stat(), t_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 × 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    -0.0918   0.0834

# one-sample proportion test for hypothesized null
# proportion of college completion of .2
prop_test(gss,
          college ~ NULL,
          p = .2)
#> # A tibble: 1 × 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 × 3
#>   statistic  p_value alternative
#>       <dbl>    <dbl> <chr>      
#> 1      8.27 1.30e-16 two.sided