Description Usage Arguments Details Value References See Also Examples
dabest
applies a summary function (func
, default
mean
) to the groups listed in idx
, which
are factors/strings in the x
column of .data
. The first element
of idx
is the control group. The difference between
func(group_n)
and func(control)
is computed, for every
subsequent element of idx
.
For each comparison, a bootstrap
confidence interval is constructed for the difference, and bias correction and
acceleration is applied to correct for any skew. dabest
uses bootstrap
resampling to compute nonparametric assumptionfree confidence intervals,
and visualizes them using estimation plots with a specialized
plot.dabest
function.
1 2 
.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 
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 TRUE. 
ci 
float, default 95. The level of the confidence intervals produced.
The default 
reps 
integer, default 5000. The number of bootstrap resamples that will be generated. 
func 
function, default mean. This function will be applied to

seed 
integer, default 12345. This specifies the seed used to set the random number generator. Setting a seed ensures that the bootstrap confidence intervals for the same data will remain stable over separate runs/calls of this function. See set.seed for more details. 
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.
A list with 7 elements: data
, x
, y
, idx
,
id.column
, result
, and summary
.
data
, x
, y
, id.column
, and idx
are the
same keywords supplied to dabest
as noted above.
x
and y
are quoted variables for tidy evaluation by plot
.
summary
is a tibble with func
applied to every
group specified in idx
. These will be used by plot()
to
generate the estimation plot.
result
is a tibble with the following 15 columns:
control_group, test_group 
The name of the control group and test group respectively. 
control_size, test_size 
The number of observations in the control group and test group respectively. 
func 
The 
paired 
Is
the difference paired ( 
difference 
The difference between the two groups; effectively

variable 
The
variable whose difference is being computed, ie. the column supplied to

ci 
The 
bca_ci_low, bca_ci_high 
The lower and upper limits of the Bias Corrected and Accelerated bootstrap confidence interval. 
pct_ci_low, pct_ci_high 
The lower and upper limits of the percentile bootstrap confidence interval. 
bootstraps 
The array of bootstrap resamples generated. 
Bootstrap Confidence Intervals. DiCiccio, Thomas J., and Bradley Efron. Statistical Science: vol. 11, no. 3, 1996. pp. 189–228.
An Introduction to the Bootstrap. Efron, Bradley, and R. J. Tibshirani. 1994. CRC Press.
plot.dabest
, which generates an estimation plot from
the dabest
object.
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65  # 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
# Produce an estimation plot.
plot(unpaired_mean_diff)
# 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
)
# Computing the median difference.
unpaired_median_diff < dabest(
iris, Species, Petal.Width,
idx = c("setosa", "versicolor", "virginica"),
paired = FALSE,
func = median
)
# Producing a 90% CI instead of 95%.
unpaired_mean_diff_90_ci < dabest(
iris, Species, Petal.Width,
idx = c("setosa", "versicolor", "virginica"),
paired = FALSE,
ci = 0.90
)
# 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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.