meanBoot

data

is a vector of TRUE/FALSE indices. It allows boot to select samples.

indices

meanBoot is a support function for bootstrapping method.
Its main purpose is to compute a mean of a given samples from <code>data</code> selected by <code>indices</code>.

A non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.

Chainarong Amornbunchornvej

EDOIF

Empirical Distribution Ordering Inference Framework (EDOIF)

meanBoot function

<dl><dt>data</dt>
<dd>is a vector of real-number values</dd>
<dt>indices</dt>
<dd>is a vector of TRUE/FALSE indices. It allows boot to select samples.</dd></dl>

Arguments

meanBoot function — meanBoot

<dl>

<dt>data</dt>
<dd>is a vector of real-number values</dd>


<dt>indices</dt>
<dd>is a vector of TRUE/FALSE indices. It allows boot to select samples.</dd>

</dl>

meanBoot: meanBoot function

Description

Usage

Value

Arguments