Analysis results data for paired and non-paired Wilcoxon Rank-Sum tests.
Arguments
- data
(
data.frame
)
a data frame. See below for details.- by
(
tidy-select
)
column name to compare by.- variable
(
tidy-select
)
column name to be compared.- ...
arguments passed to
wilcox.test(...)
- id
(
tidy-select
)
column name of the subject or participant ID.
Details
For the ard_wilcoxtest()
function, the data is expected to be one row per subject.
The data is passed as wilcox.test(data[[variable]] ~ data[[by]], paired = FALSE, ...)
.
For the ard_paired_wilcoxtest()
function, the data is expected to be one row
per subject per by level. Before the test is calculated, the data are
reshaped to a wide format to be one row per subject.
The data are then passed as
wilcox.test(x = data_wide[[<by level 1>]], y = data_wide[[<by level 2>]], paired = TRUE, ...)
.
Examples
cards::ADSL |>
dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
ard_wilcoxtest(by = "ARM", variable = "AGE")
#> {cards} data frame: 5 x 9
#> group1 variable context stat_name stat_label stat
#> 1 ARM AGE ttest statistic X-square… 3862.5
#> 2 ARM AGE ttest p.value p-value 0.435
#> 3 ARM AGE ttest method method Wilcoxon…
#> 4 ARM AGE ttest alternative alternat… two.sided
#> 5 ARM AGE ttest paired Paired t… FALSE
#> ℹ 3 more variables: fmt_fn, warning, error
# constructing a paired data set,
# where patients receive both treatments
cards::ADSL[c("ARM", "AGE")] |>
dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
dplyr::mutate(.by = ARM, USUBJID = dplyr::row_number()) |>
dplyr::arrange(USUBJID, ARM) |>
ard_paired_wilcoxtest(by = ARM, variable = AGE, id = USUBJID)
#> {cards} data frame: 5 x 9
#> group1 variable context stat_name stat_label stat
#> 1 ARM AGE ttest statistic X-square… 1754
#> 2 ARM AGE ttest p.value p-value 0.522
#> 3 ARM AGE ttest method method Wilcoxon…
#> 4 ARM AGE ttest alternative alternat… two.sided
#> 5 ARM AGE ttest paired Paired t… TRUE
#> ℹ 3 more variables: fmt_fn, warning, error