View source: R/ard_effectsize_cohens_d.R
ard_effectsize_cohens_d | R Documentation |
Analysis results data for paired and non-paired Cohen's D Effect Size Test
using effectsize::cohens_d()
.
ard_effectsize_cohens_d(data, by, variables, conf.level = 0.95, ...)
ard_effectsize_paired_cohens_d(data, by, variables, id, conf.level = 0.95, ...)
data |
( |
by |
( |
variables |
( |
conf.level |
(scalar |
... |
arguments passed to |
id |
( |
For the ard_effectsize_cohens_d()
function, the data is expected to be one row per subject.
The data is passed as effectsize::cohens_d(data[[variable]]~data[[by]], data, paired = FALSE, ...)
.
For the ard_effectsize_paired_cohens_d()
function, the data is expected to be one row
per subject per by level. Before the effect size is calculated, the data are
reshaped to a wide format to be one row per subject.
The data are then passed as
effectsize::cohens_d(x = data_wide[[<by level 1>]], y = data_wide[[<by level 2>]], paired = TRUE, ...)
.
ARD data frame
cards::ADSL |>
dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
ard_effectsize_cohens_d(by = ARM, variables = AGE)
# 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) |>
dplyr::group_by(USUBJID) |>
dplyr::filter(dplyr::n() > 1) |>
ard_effectsize_paired_cohens_d(by = ARM, variables = AGE, id = USUBJID)
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