ena.rotate.by.mean

Computes a dimensional reduction from a matrix of points such that
the first dimension of the projected space passes through the means of two
groups in a the original space. Subsequent dimensions of the projected space
are computed using ena.svd

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).

Cody L Marquart

rENA

Epistemic Network Analysis

Zachari Swiecki

Wesley Collier

Brendan Eagan

Roman Woodward

David Williamson Shaffer

ena.rotate.by.mean function

<dl><dt>enaset</dt>
<dd>An <code>ENAset</code></dd>
<dt>groups</dt>
<dd>A list containing two logical vectors of length <code>nrow(ENA.set$ena.data$units)</code>,
where each vector defines whether a unit is in one of the two groups whose means
are used to determine the dimensional reduction</dd></dl>

Arguments

ENA Rotate by mean — ena.rotate.by.mean

<dl>

<dt>enaset</dt>
<dd>An <code>ENAset</code></dd>


<dt>groups</dt>
<dd>A list containing two logical vectors of length <code>nrow(ENA.set$ena.data$units)</code>,
where each vector defines whether a unit is in one of the two groups whose means
are used to determine the dimensional reduction</dd>

</dl>

ena.rotate.by.mean: ENA Rotate by mean

Description

Usage

Value

Arguments