dabest prepares a
tidy dataset for analysis
using estimation statistics.
A data.frame or tibble.
A vector containing factors or strings in the
Boolean, default FALSE. If TRUE, the two groups are treated as paired samples. The first group is treated as pre-intervention and the second group is considered post-intervention.
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
non-parametric assumption-free confidence intervals. Visualization of the
effect sizes and their confidence intervals using estimation plots is then
performed with a specialized plotting function.
dabest object with 8 elements.
The dataset passed to
dabest, stored here
The vector of control-test groupings. For each pair in
idx, an effect size will be computed by downstream
functions used to compute effect sizes (such as
Whether or not the experiment consists of paired (aka repeated) observations.
TRUE, the column in
data that indicates the pairing of observations.
The variable name of the dataset passed to
All groups as indicated in the
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 user-friendly 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 within-subject # 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()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.