The library `superb`

offers two main functionalities.
First, it can be used to obtain plots with adjusted error bars. The main
function is `superbPlot()`

but you can also use
`superbShiny()`

for a graphical user interface requiring no
programming nor scripting.

The purpose of `superbPlot()`

is to provide a plot with
summary statistics and correct error bars. With simple adjustments, the
error bar are adjusted to the design (within or between), to the purpose
(single or pair-wise differences), to the sampling method (simple
randomized samples or cluster randomized samples) and to the population
size (infinite or of a specific size). The `superbData()`

function does not generate the plot but returns the summary statistics
and the interval boundaries. These can afterwards be sent to other
plotting environment.

The second functionality is to generate random datasets. The function
`GRD()`

is used to easily generate random data from any
design (within or between) using any population distribution with any
parameters, and with various effect sizes. `GRD()`

is useful
to test statistical procedures and plotting procedures such as
`superbPlot()`

.

The official **CRAN** version can be installed with

```
install.packages("superb")
library(superb)
```

The development version can be accessed through GitHub:

```
::install_github("dcousin3/superb")
devtoolslibrary(superb)
```

The easiest is to use the graphical interface which can be launched with

`superbShiny()`

The following examples use the script-based commands.

Here is a simple example illustrating the `ToothGrowth`

dataset of rats (in which the dependent variable is `len`

) as
a function of the `dose`

of vitamin and the form of the
vitamin supplements `supp`

(pills or juice)

```
superbPlot(ToothGrowth,
BSFactors = c("dose","supp"),
variables = "len" )
```

In the above, the default summary statistic, the mean, is used. The
error bars are, by default, the 95% confidence intervals. These two
choices can be changed with the `statistic`

and the
`errorbar`

arguments.

This second example explicitly indicates to display the
`median`

instead of the default `mean`

summary
statistics

```
superbPlot(ToothGrowth,
BSFactors = c("dose","supp"),
variables = "len",
statistic = "median")
```

As a third example, we illustrate the harmonic means
`hmedian`

along with 99.9% confidence intervals using
lines:

```
superbPlot(ToothGrowth,
BSFactors = c("dose","supp"),
variables = "len",
statistic = "hmean",
errorbar = "CI", gamma = 0.999,
plotStyle = "line")
```

The second function, `GRD()`

, can be used to generate
random data from designs with various within- and between-subject
factors. This example generates scores for 300 simulated participants in
a 3 x 2 design with repeated-measures on `Day`

s. Only the
factor `Day`

is simulated to improve the scores by reducing
it:

```
<- GRD(
testdata RenameDV = "score",
SubjectsPerGroup = 100,
BSFactors = "Difficulty(A,B,C)",
WSFactors = "Day(2)",
Population = list(mean = 75,stddev = 12,rho = 0.5),
Effects = list("Day" = slope(-3) )
)head(testdata)
```

```
## id Difficulty score.1 score.2
## 1 1 A 87.50433 102.15152
## 2 2 A 60.07547 51.97630
## 3 3 A 75.34846 59.80454
## 4 4 A 60.93139 55.34069
## 5 5 A 81.91479 86.32453
## 6 6 A 83.31082 84.09887
```

The simulated scores are illustrated using using a more elaborated
layout, the `pointjitterviolin`

which, in addition to the
mean and confidence interval, shows the raw data using jitter dots and
the distribution using a violin plot:

```
superbPlot(testdata,
BSFactors = "Difficulty",
WSFactors = "Day(2)",
variables = c("score.1","score.2"),
plotStyle = "pointjitterviolin",
errorbarParams = list(color = "purple"),
pointParams = list( size = 3, color = "purple")
)
```

In the above example, optional arguments `errorbarParams`

and `pointParams`

are used to inject specifications in the
error bars and the points respectively. When these arguments are used,
they override the defaults from `superbPlot()`

.

As seen, the library `superb`

makes it easy to illustrate
summary statistics along with the error bars. Some layouts can be used
to visualize additional characteristics of the raw data. Finally, the
resulting appearance can be customized in various ways.

The complete documentation is available on this site.

A general introduction to the `superb`

framework
underlying this library is in press at *Advances in Methods and
Practices in Psychological Sciences* (Cousineau, Goulet, &
Harding, in press).

Cousineau D, Goulet M, Harding B (2021). “Summary plots with adjusted
error bars: The superb framework with an implementation in R.”
*Advances in Methods and Practices in Psychological Science*,
**2021**, 1–46. doi: https://doi.org/10.1177/25152459211035109

Walker, J. A. L. (2021). “Summary plots with adjusted error bars
(superb).” *Youtube video*,
**accessible
here)**.