ecdfcis

Confidence Intervals for Empirical Cumulative Distribution Functions

A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.

John E Kolassa

MultNonParam

Multivariate Nonparametric Methods

John E. Kolassa

Stephane Jankowski

ecdfcis function

<dl><dt>data</dt>
<dd>vector of observations</dd>
<dt>alpha</dt>
<dd>1-confidence level.</dd>
<dt>dataname</dt>
<dd>Name of variable for use in axis labeling</dd>
<dt>exact</dt>
<dd>logical value controlling whether confidence intervals are exact or asymlptotic.</dd>
<dt>newplot</dt>
<dd>logical value controlling whether the estimate is added to an existing plot, or whether a new plot should be constructed.</dd></dl>

Arguments

Confidence Intervals for Empirical Cumulative Distribution Functions — ecdfcis

<dl>

<dt>data</dt>
<dd>vector of observations</dd>


<dt>alpha</dt>
<dd>1-confidence level.</dd>


<dt>dataname</dt>
<dd>Name of variable for use in axis labeling</dd>


<dt>exact</dt>
<dd>logical value controlling whether confidence intervals are exact or asymlptotic.</dd>


<dt>newplot</dt>
<dd>logical value controlling whether the estimate is added to an existing plot, or whether a new plot should be constructed.</dd>

</dl>

ecdfcis: Confidence Intervals for Empirical Cumulative Distribution Functions

Description

Usage

Arguments