Using dabestr

How to create estimation plots

Joses Ho

2019-01-05

Create Data

For this vignette, we will create and use a synthetic dataset.

library(dplyr)

set.seed(54321)

N = 40
c1 <- rnorm(N, mean = 100, sd = 25)
c2 <- rnorm(N, mean = 100, sd = 50)
g1 <- rnorm(N, mean = 120, sd = 25)
g2 <- rnorm(N, mean = 80, sd = 50)
g3 <- rnorm(N, mean = 100, sd = 12)
g4 <- rnorm(N, mean = 100, sd = 50)
gender <- c(rep('Male', N/2), rep('Female', N/2))
id <- 1: N


wide.data <- 
  tibble::tibble(
    Control1 = c1, Control2 = c2,
    Group1 = g1, Group2 = g2, Group3 = g3, Group4 = g4,
    Gender = gender, ID = id)


my.data   <- 
  wide.data %>%
  tidyr::gather(key = Group, value = Measurement, -ID, -Gender)

head(my.data)
## # A tibble: 6 x 4
##   Gender    ID Group    Measurement
##   <chr>  <int> <chr>          <dbl>
## 1 Male       1 Control1        95.5
## 2 Male       2 Control1        76.8
## 3 Male       3 Control1        80.4
## 4 Male       4 Control1        58.7
## 5 Male       5 Control1        89.8
## 6 Male       6 Control1        72.6

This dataset is a tidy dataset, where each observation (datapoint) is a row, and each variable (or associated metadata) is a column. dabestr requires that data be in this form, as do other popular R packages for data visualization and analysis.

The Gardner-Altman Two Group Estimation Plot

Unpaired

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

library(dabestr)

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 
## DABEST (Data Analysis with Bootstrap Estimation) v0.2.0
## =======================================================
## 
## Variable: Measurement 
## 
## Unpaired mean difference of Group1 (n=40) minus Control1 (n=40)
##  19.2 [95CI  7.92; 30.4]
## 
## 
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.

To create a two-group estimation plot (aka a Gardner-Altman plot), simply use plot(dabest.object).

Advanced R users would be interested to learn that dabest produces an object of class dabest. There is a generic S3 plot method for dabest objects that produces the estimation plot.

plot(two.group.unpaired, 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 in this vignette.

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 
## DABEST (Data Analysis with Bootstrap Estimation) v0.2.0
## =======================================================
## 
## Variable: Measurement 
## 
## Paired mean difference of Group1 (n=40) minus Control1 (n=40)
##  19.2 [95CI  7.08; 30.9]
## 
## 
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.
plot(two.group.paired, color.column = Gender)

The Cummings estimation plot

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
         )

multi.two.group.unpaired 
## DABEST (Data Analysis with Bootstrap Estimation) v0.2.0
## =======================================================
## 
## Variable: Measurement 
## 
## Unpaired mean difference of Group1 (n=40) minus Control1 (n=40)
##  19.2 [95CI  7.92; 30.4]
## 
## Unpaired mean difference of Group2 (n=40) minus Control2 (n=40)
##  -23.9 [95CI  -45.1; -4.08]
## 
## 
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.
plot(multi.two.group.unpaired, color.column = Gender)