Description Usage Arguments Details Value See Also Examples
dabest
prepares a
tidy dataset for analysis
using estimation statistics.
1 
.data 
A data.frame or tibble. 
x, y 
Columns in 
idx 
A vector containing factors or strings in the 
paired 
Boolean, default FALSE. If TRUE, the two groups are treated as paired samples. The first group is treated as preintervention and the second group is considered postintervention. 
id.column 
Default NULL. A column name indicating the identity of the
datapoint if the data is paired. This must be supplied if paired is

Estimation statistics is a statistical framework that focuses on effect sizes and confidence intervals around them, rather than P values and associated dichotomous hypothesis testing.
dabest
() collates the data in preparation for the computation of
effect sizes. Bootstrap resampling is used to compute
nonparametric assumptionfree confidence intervals. Visualization of the
effect sizes and their confidence intervals using estimation plots is then
performed with a specialized plotting function.
A dabest
object with 8 elements.
data
The dataset passed to dabest
, stored here
as a tibble
.
x
and y
The columns in data
used to plot the x
and y axes, respectively, as supplied to dabest
. These are
quoted variables for
tidy evaluation during the
computation of effect sizes.
idx
The vector of controltest groupings. For each pair in
idx
, an effect size will be computed by downstream dabestr
functions used to compute effect sizes (such as
mean_diff()
.
is.paired
Whether or not the experiment consists of paired (aka repeated) observations.
id.column
If is.paired
is TRUE
, the column in
data
that indicates the pairing of observations.
.data.name
The variable name of the dataset passed to
dabest
.
.all.groups
All groups as indicated in the idx
argument.
Effect size computation from the loaded data.
Generating estimation plots after effect size computation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49  # Performing unpaired (two independent groups) analysis.
unpaired_mean_diff < dabest(iris, Species, Petal.Width,
idx = c("setosa", "versicolor"),
paired = FALSE)
# Display the results in a userfriendly format.
unpaired_mean_diff
# Compute the mean difference.
mean_diff(unpaired_mean_diff)
# Plotting the mean differences.
mean_diff(unpaired_mean_diff) %>% plot()
# Performing paired analysis.
# First, we munge the `iris` dataset so we can perform a withinsubject
# comparison of sepal length vs. sepal width.
new.iris < iris
new.iris$ID < 1: length(new.iris)
setosa.only <
new.iris %>%
tidyr::gather(key = Metric, value = Value, ID, Species) %>%
dplyr::filter(Species %in% c("setosa"))
paired_mean_diff < dabest(setosa.only, Metric, Value,
idx = c("Sepal.Length", "Sepal.Width"),
paired = TRUE, id.col = ID) %>%
mean_diff()
# Using pipes to munge your data and then passing to `dabest`.
# First, we generate some synthetic data.
set.seed(12345)
N < 70
c < rnorm(N, mean = 50, sd = 20)
t1 < rnorm(N, mean = 200, sd = 20)
t2 < rnorm(N, mean = 100, sd = 70)
long.data < tibble::tibble(Control = c, Test1 = t1, Test2 = t2)
# Munge the data using `gather`, then pass it directly to `dabest`
meandiff < long.data %>%
tidyr::gather(key = Group, value = Measurement) %>%
dabest(x = Group, y = Measurement,
idx = c("Control", "Test1", "Test2"),
paired = FALSE) %>%
mean_diff()

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