print.HCPC

Print the Hierarchical Clustering on Principal Components (HCPC) results.

print

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

print.HCPC function

<dl> <dt>x</dt>
<dd>an object of class HCPC</dd> <dt>file</dt>
<dd>A connection, or a character string naming the file to print to. If NULL (the default), the results are not printed in a file</dd> <dt>sep</dt>
<dd>character string to insert between the objects to print (if the argument file is not NULL</dd> <dt>...</dt>
<dd>further arguments passed to or from other methods</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

Print the Hierarchical Clustering on Principal Components (HCPC) results — print.HCPC

<dl>

 <dt>x</dt>
<dd>an object of class HCPC</dd>

 <dt>file</dt>
<dd>A connection, or a character string naming the file to print to. If NULL (the default), the results are not printed in a file</dd>

 <dt>sep</dt>
<dd>character string to insert between the objects to print (if the argument file is not NULL</dd>

 <dt>...</dt>
<dd>further arguments passed to or from other methods</dd>

</dl>

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

print.HCPC: Print the Hierarchical Clustering on Principal Components (HCPC) results

Description

Usage

Arguments

Author

See Also