svd.triplet

Compute the singular-value decomposition of a rectangular matrix with weights for rows and columns.

algebra

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

svd.triplet function

<dl> <dt>X</dt>
<dd>a data matrix</dd> <dt>row.w</dt>
<dd>vector with the weights of each row (NULL by default and the weights are uniform)</dd> <dt>col.w</dt>
<dd>vector with the weights of each column (NULL by default and the weights are uniform)</dd> <dt>ncp</dt>
<dd>the number of components kept for the outputs</dd></dl>

Arguments

Singular Value Decomposition of a Matrix — svd.triplet

<dl>

 <dt>X</dt>
<dd>a data matrix</dd>

 <dt>row.w</dt>
<dd>vector with the weights of each row (NULL by default and the weights are uniform)</dd>

 <dt>col.w</dt>
<dd>vector with the weights of each column (NULL by default and the weights are uniform)</dd>

 <dt>ncp</dt>
<dd>the number of components kept for the outputs</dd>

</dl>

svd.triplet: Singular Value Decomposition of a Matrix

Description

Usage

Value

Arguments

See Also