Usage
test_proportion_diff(
  lyt,
  vars,
  variables = list(strata = NULL),
  method = c("chisq", "schouten", "fisher", "cmh"),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  var_labels = vars,
  show_labels = "hidden",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
s_test_proportion_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  method = c("chisq", "schouten", "fisher", "cmh")
)
a_test_proportion_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  method = c("chisq", "schouten", "fisher", "cmh")
)Arguments
- lyt
 (
PreDataTableLayouts)
layout that analyses will be added to.- vars
 (
character)
variable names for the primary analysis variable to be iterated over.- variables
 (named
listofstring)
list of additional analysis variables.- method
 (
string)
one ofchisq,cmh,fisher, orschouten; specifies the test used to calculate the p-value.- na_str
 (
string)
string used to replace allNAor empty values in the output.- nested
 (
flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.- ...
 additional arguments for the lower level functions.
- var_labels
 (
character)
variable labels.- show_labels
 (
string)
label visibility: one of "default", "visible" and "hidden".- table_names
 (
character)
this can be customized in the case that the samevarsare analyzed multiple times, to avoid warnings fromrtables.- .stats
 (
character)
statistics to select for the table. Runget_stats("test_proportion_diff")to see available statistics for this function.- .formats
 (named
characterorlist)
formats for the statistics. See Details inanalyze_varsfor 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.- df
 (
data.frame)
data set containing all analysis variables.- .var
 (
string)
single variable name that is passed byrtableswhen requested by a statistics function.- .ref_group
 (
data.frameorvector)
the data corresponding to the reference group.- .in_ref_col
 (
flag)TRUEwhen working with the reference level,FALSEotherwise.
Value
test_proportion_diff()returns a layout object suitable for passing to further layouting functions, or tortables::build_table(). Adding this function to anrtablelayout will add formatted rows containing the statistics froms_test_proportion_diff()to the table layout.
s_test_proportion_diff()returns a namedlistwith a single itempvalwith an attributelabeldescribing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.
a_test_proportion_diff()returns the corresponding list with formattedrtables::CellValue().
Functions
test_proportion_diff(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::analyze().s_test_proportion_diff(): Statistics function which tests the difference between two proportions.a_test_proportion_diff(): Formatted analysis function which is used asafunintest_proportion_diff().
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))
)
# With `rtables` pipelines.
l <- basic_table() %>%
  split_cols_by(var = "grp", ref_group = "B") %>%
  test_proportion_diff(
    vars = "rsp",
    method = "cmh", variables = list(strata = "strata")
  )
build_table(l, df = dta)
#>                                              A      B
#> —————————————————————————————————————————————————————
#>   p-value (Cochran-Mantel-Haenszel Test)   1.0000    
