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Difference test for two proportions

Source: R/test_proportion_diff.R
prop_diff_test.Rd

[Stable]

The analysis function a_test_proportion_diff() can be used to create a layout element to test the difference between two proportions. The primary analysis variable, vars, indicates whether a response has occurred for each record. See the method parameter for options of methods to use to calculate the p-value. Additionally, a stratification variable can be supplied via the strata element of the variables argument. The argument alternative specifies the direction of the alternative hypothesis.

Usage

a_test_proportion_diff(
  df,
  .var,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

df

(data.frame)
data set containing all analysis variables.

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

ref_path

(character)
global reference group specification, see get_ref_info().

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

...

Additional arguments passed to tern::s_test_proportion_diff(), including:

  • method (string)
    one of chisq, cmh, cmh_wh, fisher or schouten; specifies the test used to calculate the p-value.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the 'auto' setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Value

Functions

  • a_test_proportion_diff(): Formatted analysis function which is used as afun

Note

This function has been forked from the tern package. Additional features are:

  • Additional ref_path argument for flexible reference column path specification.

Examples

dta <- data.frame(
  rsp = sample(c(TRUE, FALSE), 100, TRUE),
  grp = factor(rep(c("A", "B"), each = 50)),
  strata = factor(rep(c("V", "W", "X", "Y", "Z"), each = 20))
)

l <- basic_table() |>
  split_cols_by(var = "grp") |>
  analyze(
    vars = "rsp",
    afun = a_test_proportion_diff,
    show_labels = "hidden",
    extra_args = list(
      method = "cmh",
      variables = list(strata = "strata"),
      ref_path = c("grp", "B")
    )
  )

build_table(l, df = dta)
#>                                              A     B
#> ————————————————————————————————————————————————————
#>   p-value (Cochran-Mantel-Haenszel Test)   0.374