Calculate and add a p-value comparing the two variables in the cross table. If missing levels are included in the tables, they are also included in p-value calculation.
Usage
# S3 method for class 'tbl_cross'
add_p(
x,
test = NULL,
pvalue_fun = ifelse(source_note, label_style_pvalue(digits = 1, prepend_p = TRUE),
label_style_pvalue(digits = 1)),
source_note = FALSE,
test.args = NULL,
...
)
Arguments
- x
(
tbl_cross
)
Object with classtbl_cross
created with thetbl_cross()
function- test
(
string
)
A string specifying statistical test to perform. Default is"chisq.test"
when expected cell counts >=5 and "fisher.test
" when expected cell counts <5.- pvalue_fun
(
function
)
Function to round and format p-value. Default islabel_style_pvalue(digits = 1)
, except whensource_note = TRUE
when the default islabel_style_pvalue(digits = 1, prepend_p = TRUE)
- source_note
(scalar
logical
)
Logical value indicating whether to show p-value in the {gt} table source notes rather than a column.- test.args
(named
list
)
Named list containing additional arguments to pass to the test (if it accepts additional arguments). For example, add an argument for a chi-squared test withtest.args = list(correct = TRUE)
- ...
These dots are for future extensions and must be empty.
Examples
# Example 1 ----------------------------------
trial |>
tbl_cross(row = stage, col = trt) |>
add_p()
Drug A
Drug B
1 Pearson’s Chi-squared test
# Example 2 ----------------------------------
trial |>
tbl_cross(row = stage, col = trt) |>
add_p(source_note = TRUE)
Drug A
Drug B
Pearson’s Chi-squared test, p=0.9