dimdesc

This function is designed to point out the variables and the categories that are the most characteristic according 
 to each dimension obtained by a Factor Analysis.

multivariate

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Francois Husson

FactoMineR

Multivariate Exploratory Data Analysis and Data Mining

dimdesc function

<dl> <dt>res</dt>
<dd>an object of class PCA, MCA, CA, MFA or HMFA</dd> <dt>axes</dt>
<dd>a vector with the dimensions to describe</dd> <dt>proba</dt>
<dd>the significance threshold considered to characterized the dimension (by default 0.05)</dd></dl>

Arguments

Francois Husson <a href="/link/francois.husson%40institut-agro.fr?package=FactoMineR&version=2.9" data-mini-rdoc="FactoMineR::francois.husson@institut-agro.fr">francois.husson@institut-agro.fr</a>

Author

This function is designed to point out the variables and the categories that are the most characteristic according 
 to each dimension obtained by a Factor Analysis.

Dimension description — dimdesc

<dl>

 <dt>res</dt>
<dd>an object of class PCA, MCA, CA, MFA or HMFA</dd>

 <dt>axes</dt>
<dd>a vector with the dimensions to describe</dd>

 <dt>proba</dt>
<dd>the significance threshold considered to characterized the dimension (by default 0.05)</dd>

</dl>

Francois Husson <a href='mailto:francois.husson@institut-agro.fr'>francois.husson@institut-agro.fr</a>

dimdesc: Dimension description

Description

Usage

Value

Arguments

Author

References

See Also

Examples