Unpaired

The dabest function is the main workhorse of the dabestr package. To create a two-group estimation plot (aka a Gardner-Altman plot), we must first specify the following:

• the x and y columns,
• whether the comparison is paired = TRUE or paired = FALSE,
• and the groups to be compared via idx.

library(dabestr)

## Loading required package: magrittr

two.group.unpaired <- 
  my.data %>%
  dabest(Group, Measurement, 
         # The idx below passes "Control" as the control group, 
         # and "Group1" as the test group. The mean difference
         # will be computed as mean(Group1) - mean(Control1).
         idx = c("Control1", "Group1"), 
         paired = FALSE)

# Calling the object automatically prints out a summary.
two.group.unpaired 

## dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
## =============================================================
## 
## Good morning!
## The current time is 11:27 AM on Monday July 13, 2020.
## 
## Dataset    :  .
## The first five rows are:
## # A tibble: 5 x 5
##   Dummy Gender    ID Group    Measurement
##   <chr> <chr>  <int> <fct>          <dbl>
## 1 Dummy Male       1 Control1        95.5
## 2 Dummy Male       2 Control1        76.8
## 3 Dummy Male       3 Control1        80.4
## 4 Dummy Male       4 Control1        58.7
## 5 Dummy Male       5 Control1        89.8
## 
## X Variable :  Group
## Y Variable :  Measurement
## 
## Effect sizes(s) will be computed for:
##   1. Group1 minus Control1

To compute the mean difference between Group1 and Control1, we apply the mean_diff() function to the dabest object created above.

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)

# Calling the above object produces a textual summary of the computed effect size.
two.group.unpaired.meandiff

## dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
## =============================================================
## 
## Good morning!
## The current time is 11:27 AM on Monday July 13, 2020.
## 
## Dataset    :  .
## X Variable :  Group
## Y Variable :  Measurement
## 
## Unpaired mean difference of Group1 (n = 40) minus Control1 (n = 40)
##  19.2 [95CI  7.62; 30.6]
## 
## 
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.

As of dabest v0.3.0, there are five effect sizes available:

• The mean difference, given by mean_diff().
• The median difference, given by median_diff().
• Cohen’s d, given by cohens_d().
• Hedges’ g, given by hedges_g().
• Cliff’s delta, given by cliffs_delta().

To create a two-group estimation plot (aka a Gardner-Altman plot) from this data, simply use plot(dabest_effsize.object).

plot(two.group.unpaired.meandiff, color.column = Gender)

This is known as a Gardner-Altman estimation plot, after Martin J. Gardner and Douglas Altman who were the first to publish it in 1986.

The key features of the Gardner-Altman estimation plot are:

1. All data points are plotted.
2. The mean difference (the effect size) and its 95% confidence interval (95% CI) is displayed as a point estimate and vertical bar respectively, on a separate but aligned axes.

The estimation plot produced by dabest differs from the one first introduced by Gardner and Altman in one important aspect. dabest derives the 95% CI through nonparametric bootstrap resampling. This enables visualization of the confidence interval as a graded sampling distribution.

The 95% CI presented is bias-corrected and accelerated (ie. a BCa bootstrap). You can read more about bootstrap resampling and BCa correction here.

You can also obtain Gardner-Altman plots for the median difference, Cohen’s d, and Hedges’ g effect sizes. Below we demonstrate how to obtain one for the Hedges’ g of the loaded two.group.unpaired dataset.

two.group.unpaired %>% hedges_g() %>% plot(color.column = Gender)

Paired

If you have paired or repeated observations, you must specify the id.col, a column in the data that indicates the identity of each paired observation. This will produce a Tufte slopegraph instead of a swarmplot.

two.group.paired <- 
  my.data %>%
  dabest(Group, Measurement, 
         idx = c("Control1", "Group1"), 
         paired = TRUE, id.col = ID)


# The summary indicates this is a paired comparison. 
two.group.paired

## dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
## =============================================================
## 
## Good morning!
## The current time is 11:27 AM on Monday July 13, 2020.
## 
## Dataset    :  .
## The first five rows are:
## # A tibble: 5 x 5
##   Dummy Gender    ID Group    Measurement
##   <chr> <chr>  <int> <fct>          <dbl>
## 1 Dummy Male       1 Control1        95.5
## 2 Dummy Male       2 Control1        76.8
## 3 Dummy Male       3 Control1        80.4
## 4 Dummy Male       4 Control1        58.7
## 5 Dummy Male       5 Control1        89.8
## 
## X Variable :  Group
## Y Variable :  Measurement
## 
## Paired effect size(s) will be computed for:
##   1. Group1 minus Control1

# Create a paired plot.
two.group.paired %>% 
  mean_diff() %>% 
  plot(color.column = Gender)

Multi-two group

To create a multi-two group plot, one will need to specify a list, with each element of the list corresponding to the each two-group comparison.

multi.two.group.unpaired <- 
  my.data %>%
  dabest(Group, Measurement, 
         idx = list(c("Control1", "Group1"), 
                    c("Control2", "Group2")),
         paired = FALSE)


# Compute the mean difference.
multi.two.group.unpaired.meandiff <- mean_diff(multi.two.group.unpaired)


# Create a multi-two group plot.
multi.two.group.unpaired.meandiff %>% 
  plot(color.column = Gender)