An utility function to visualise the output from `repDiversity`

.

```
# S3 method for immunr_chao1
vis(
.data,
.by = NA,
.meta = NA,
.errorbars = c(0.025, 0.975),
.errorbars.off = FALSE,
.points = TRUE,
.test = TRUE,
.signif.label.size = 3.5,
...
)
```

## Arguments

- .data
Output from `repDiversity`

.

- .by
Pass NA if you want to plot samples without grouping.

You can pass a character vector with one or several column names from ".meta"
to group your data before plotting. In this case you should provide ".meta".

You can pass a character vector that exactly matches the number of samples in
your data, each value should correspond to a sample's property. It will be used
to group data based on the values provided. Note that in this case you should
pass NA to ".meta".

- .meta
A metadata object. An R dataframe with sample names and their properties,
such as age, serostatus or hla.

- .errorbars
A numeric vector of length two with quantiles for error bars
on sectors. Disabled if ".errorbars.off" is TRUE.

- .errorbars.off
If TRUE then plot CI bars for distances between each group.
Disabled if no group passed to the ".by" argument.

- .points
A logical value defining whether points will be visualised or not.

- .test
A logical vector whether statistical tests should be applied. See "Details" for more information.

- .signif.label.size
An integer value defining the size of text for p-value.

- ...
Not used here.

## Details

If data is grouped, then statistical tests for comparing means of groups will be performed, unless `.test = FALSE`

is supplied.
In case there are only two groups, the Wilcoxon rank sum test (https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test) is performed
(R function `wilcox.test`

with an argument `exact = FALSE`

) for testing if there is a difference in mean rank values between two groups.
In case there more than two groups, the Kruskal-Wallis test (https://en.wikipedia.org/wiki/Kruskal
A significant Kruskal-Wallis test indicates that at least one sample stochastically dominates one other sample.
Adjusted for multiple comparisons P-values are plotted on the top of groups.
P-value adjusting is done using the Holm method (https://en.wikipedia.org/wiki/Holm
You can execute the command `?p.adjust`

in the R console to see more.