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 = F, .points = T, .test = T, .signif.label.size = 3.5, ... )

.data | Output from |
---|---|

.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. |

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

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 = F`

) 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.