predict.CA

Predict the projection of new rows with Correspondence Analysis.

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

predict.CA function

<dl> <dt>object</dt>
<dd>an object of class CA</dd> <dt>newdata</dt>
<dd>A data frame or a matrix in which to look for variables with which to predict. newdata must contain columns with the same names as the original data.</dd> <dt>...</dt>
<dd>Other options.</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

Predict projection for new rows with Correspondence Analysis — predict.CA

<dl>

 <dt>object</dt>
<dd>an object of class CA</dd>

 <dt>newdata</dt>
<dd>A data frame or a matrix in which to look for variables with which to predict. newdata must contain columns with the same names as the original data.</dd>

 <dt>...</dt>
<dd>Other options.</dd>

</dl>

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

predict.CA: Predict projection for new rows with Correspondence Analysis

Description

Usage

Arguments

Author

See Also