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)

This is a Cumming estimation plot. It is heavily influenced by the plot designs of Geoff Cumming in his 2012 text Understanding the New Statistics. The effect size and 95% CIs are plotted a separate axes that is now positioned below the raw data. In addition, summary measurements are displayed as gapped lines to the right of each group. These vertical lines are identical to conventional mean ± standard deviation error bars. Here, the mean of each group is indicated as a gap in the line, drawing inspiration from Edward Tufte’s low data-ink ratio dictum.

By default, dabestr plots the mean ± standard deviation of each group as a gapped line beside each group. The group.summaries = 'median_quartiles' parameter will plot the median and 25th & 75th percentiles of each group is plotted instead. If group.summaries = NULL, the summaries are not shown.

plot(multi.two.group.unpaired.meandiff, 
     color.column = Gender, 
     group.summaries = "median_quartiles")

Multi-paired

One can also produce a multi-paired plot.

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


multi.two.group.paired.mean_diff <- mean_diff(multi.two.group.paired)


plot(multi.two.group.paired.mean_diff, 
     color.column = Gender, 
     slopegraph = TRUE)

Shared Control

If you supply a character vector to idx with more than 2 groups, a shared control plot will be produced.

shared.control <- 
  my.data %>%
  dabest(Group, Measurement, 
         idx = c("Control2", "Group2", "Group4"),
         paired = FALSE
         )

shared.control.mean_diff <- shared.control %>% mean_diff()

plot(shared.control.mean_diff, 
     color.column = Gender,
     rawplot.type = "swarmplot")

Multi-group plot

multi.group <- 
  my.data %>%
  dabest(Group, Measurement, 
         idx = list(c("Control1", "Group1", "Group3"), 
                    c("Control2", "Group2", "Group4")),
         paired = FALSE
        )

multi.group.mean_diff <- multi.group %>% mean_diff() 

plot(multi.group.mean_diff, color.column = Gender)