ena.rotate.by.hena.regression

This function allows user to provide a regression formula for rotation on x and optionally on y.
 If regression formula for y is not provide, svd is applied to the residual data deflated by x to get y coordinates.
 The regression formula uses ENA dimensions are dependent variables.
 The first predictor has to be two-group categorical, binary, or numerical.

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.hena.regression function

<dl><dt>enaset</dt>
<dd>An <code>ENAset</code></dd>
<dt>params</dt>
<dd>list of parameters, may include:
x_var: Regression formula for x direction, such as "lm(formula=V ~ Condition + GameHalf + Condition : GameHalf)",
 where V always stands for the ENA points.
y_var: Regression formula, similar to x_var, for y direction (optional).</dd></dl>

Arguments

ENA Rotate by regression — ena.rotate.by.hena.regression

<dl>

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


<dt>params</dt>
<dd>list of parameters, may include:
x_var: Regression formula for x direction, such as "lm(formula=V ~ Condition + GameHalf + Condition : GameHalf)",
 where V always stands for the ENA points.
y_var: Regression formula, similar to x_var, for y direction (optional).</dd>

</dl>

ena.rotate.by.hena.regression: ENA Rotate by regression

Description

Usage

Value

Arguments