scaleboot: Approximately Unbiased P-values via Multiscale Bootstrap

Graduate School of Informatics

Kyoto University

Jointly affiliated at RIKEN AIP

Lab website

scaleboot is an add-on package of R. This is for calculating approximately unbiased (AU) p-values from a set of multiscale bootstrap probabilities for a hypothesis. Scaling is equivalent to changing the sample size of data set in bootstrap resampling. We calculate bootstrap probabilities at several scales, from which a very accurate p-value is calculated. This multiscale bootstrap method has been implemented in CONSEL software and pvclust package. The thrust of scaleboot package is to calculate an improved version of AU p-values which are justified even for hypotheses with nonsmooth boundaries by taking care of the singularity.

scaleboot package includes an interface to pvclust package of R for bootstrapping hierarchical clustering. We use pvclust to calculate multiscale bootstrap probabilities, from which we calculate an improved version of AU p-values using scaleboot.

scaleboot has a front end for phylogenetic inference, and it can replace CONSEL software for testing phylogenetic trees. Currently, scaleboot does not have a method for file conversion of several phylogenetic software, and so we must use CONSEL for this purpose before applying scaleboot to calculate an improved version of AU p-values for trees and edges.

**The package vignette "Multiscale Bootstrap Using Scaleboot Package" (usesb.pdf) explains the methodology. It includes a simple example for illustration. It also includes real applications in hierarchical clustering and phylogenetic inference. Further description is given in Shimodaira (2008). For the use of scaleboot, Shimodaira (2008) may be referenced.**

**New in 2019. A new method "Selective Infernce" (SI) has been implemented in scaleboot (>=1.0-0) and pvclust (>=2.1-0); currently they are only in the development versions available at github sites. SI is useful for computing p-values for
edges (or clusters). The method is explained in
Shimodaira and Terada (2019),
and it may be cited for using SI value.
The theory of SI is described in
Terada and Shimodaira (2017).
Three new vignettes are also available:
"Phylogenetic Tree Selection" (phylo.pdf),
"Model Map in Phylogenetics" (modelmap.pdf),
"Computing selective inference p-values of clusters using
pvclust and scaleboot" (pvclust.pdf).
**

- scaleboot at github. The development version is at master branch.
- pvclust at github. The development version is at develop branch.

scaleboot as well as pvclust is easily installed from CRAN online. RStudio users can install the package by choosing "scaleboot" from the pull-down menu. Otherwise, run R on your computer and type

```
> install.packages("scaleboot")
```

> install.packages("pvclust")

The development version of scaleboot as well as pvclust is installed from github. You need the devtools for installing from github.

```
> install.packages("devtools") # binary version is ok
```

> library(devtools)

> install_github("shimo-lab/scaleboot", subdir = "src") # ref=master

> install_github("shimo-lab/pvclust", ref = "develop", subdir = "src")

Supplementary dataset files for phylogenetic inference are available at ` dataset/mam15-files` directory of
the github site.

- Shimodaira, H. (2002). An approximately unbiased test of phylogenetic tree selection. Systematic Biology, 51, 492-508.
- Shimodaira, H. (2004). Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling. Annals of Statistics, 32, 2616-2641. [PDF] [SUPPLEMENT]
- Shimodaira, H. (2006). Approximately Unbiased Tests for Singular Surfaces via Multiscale Bootstrap Resampling. Research Reports B-430. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Japan. [PDF]
- Shimodaira, H. (2006). Technical Details of Multiscale Bootstrap for Singular Surfaces. Research Reports B-431. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Japan. [PDF]
**Shimodaira, H. (2008). Testing Regions with Nonsmooth Boundaries via Multiscale Bootstrap.***Journal of Statistical Planning and Inference,*138, 1227-1241, 2008. http://dx.doi.org/10.1016/j.jspi.2007.04.001- Terada, R. and Shimodaira, H. (2017). Selective inference for the problem of regions via multiscale bootstrap. arXiv:1711.00949
- Shimodaira, H. and Terada, R. (2019). Selective Inference for Testing Trees and Edges in Phylogenetics. arXiv:1902.04964