randomize.mca

We sample from each of the active variables independently removing the
original correlations but retaining the frequencies of the categories. This
function is useful to see the extent to which the mca solution reflects the
correlations between variables or the frequency distribution between the
active categories. Passive categories are inherited from the original analysis.

Specific and class specific multiple correspondence analysis on
survey-like data. Soc.ca is optimized to the needs of the social scientist and
presents easily interpretable results in near publication ready quality.

Anton Larsen

soc.ca

Specific Correspondence Analysis for the Social Sciences

Jacob Lunding

Stefan Andrade

randomize.mca function

<dl><dt>r</dt>
<dd>a result object from soc.mca</dd>
<dt>replace</dt>
<dd>should sampling be with replacement? see <a href="/link/sample?package=soc.ca&version=0.8.1" data-mini-rdoc="soc.ca::sample">sample</a></dd></dl>

Arguments

Create a randomized mca on the basis of an existing mca — randomize.mca

<dl>

<dt>r</dt>
<dd>a result object from soc.mca</dd>


<dt>replace</dt>
<dd>should sampling be with replacement? see <a href='https://rdrr.io/r/base/sample.html'>sample</a></dd>

</dl>

randomize.mca: Create a randomized mca on the basis of an existing mca

Description

Usage

Value

Arguments

Examples