This package provides tools for Generating data matrices following Multinomial and Dirichlet-Multinomial distributions, Computing the following test-statistics and their corresponding p-values, and Computing the power and size of the tests described above using Monte-Carlo simulations.
| \(\textbf{Hypothesis Test}\) | \(\textbf{Test Statistics Function}\) | \(\textbf{Power Calculation Function}\) |
| 2+ Sample Means w/ Reference Vector | Xmc.sevsample |
MC.Xmc.statistics |
| 1 Sample Mean w/ Reference Vector | Xsc.onesample |
MC.Xsc.statistics |
| 2+ Sample Means w/o Reference Vector | Xmcupo.sevsample |
MC.Xmcupo.statistics |
| 2+ Sample Overdispersions | Xoc.sevsample |
MC.Xoc.statistics |
| 2+ Sample DM-Distribution | Xdc.sevsample |
MC.Xdc.statistics |
| Multinomial vs DM | C.alpha.multinomial |
MC.ZT.statistics |
In addition to hypothesis testing and power calculations you can:
Perform basic data management to exclude samples with fewer than pre-specified number of reads,
collapse rare taxa and order the taxa by frequency. This is useful to exclude failed samples
(i.e. samples with very few reads) - Data.filter
Plot your data - Barchart.data
Generate random sample of Dirichlet Multinomial data with pre-specified parameters - Dirichlet.multinomial
Note: Thought the description of the functions refer its application to ranked abundance distributions (RAD) data, every function is also applicable to model species abundance data. See references for a discussion and application to both type of ecological data.
La Rosa PS, Brooks JP, Deych E, Boone EL, Edwards DJ, et al. (2012) Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. PLoS ONE 7(12): e52078. doi:10.1371/journal.pone.0052078
Yang D, Johnson J, Zhou X, Deych E, et al. (2019) Microbiome Recursive Partitioning. Currently Under Review.