ind.sup

A vector indicating the indexes of the quantitative supplementary variables.

quanti.sup

A vector indicating the indexes of the categorical supplementary variables.

quali.sup

An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.

row.w

Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables.
Performs also Specific Multiple Correspondence Analysis with supplementary categories and supplementary categorical variables.
Missing values are treated as an additional level, categories which are rare can be ventilated.

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Alexandre Blansch<c3><a9>

fdm2id

Data Mining and R Programming for Beginners

Alexandre Blansché

MCA function

<dl><dt>d</dt>
<dd>A ddata frame or a table with n rows and p columns, i.e. a contingency table.</dd>
<dt>ncp</dt>
<dd>The number of dimensions kept in the results (by default 5).</dd>
<dt>ind.sup</dt>
<dd>A vector indicating the indexes of the supplementary individuals.</dd>
<dt>quanti.sup</dt>
<dd>A vector indicating the indexes of the quantitative supplementary variables.</dd>
<dt>quali.sup</dt>
<dd>A vector indicating the indexes of the categorical supplementary variables.</dd>
<dt>row.w</dt>
<dd>An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.</dd></dl>

Arguments

Multiple Correspondence Analysis (MCA) — MCA

<dl>

<dt>d</dt>
<dd>A ddata frame or a table with n rows and p columns, i.e. a contingency table.</dd>


<dt>ncp</dt>
<dd>The number of dimensions kept in the results (by default 5).</dd>


<dt>ind.sup</dt>
<dd>A vector indicating the indexes of the supplementary individuals.</dd>


<dt>quanti.sup</dt>
<dd>A vector indicating the indexes of the quantitative supplementary variables.</dd>


<dt>quali.sup</dt>
<dd>A vector indicating the indexes of the categorical supplementary variables.</dd>


<dt>row.w</dt>
<dd>An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.</dd>

</dl>

MCA: Multiple Correspondence Analysis (MCA)

Description

Usage

Value

Arguments

See Also

Examples