predict.PCA

Predict the projection of new rows with Principal Component 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.PCA function

<dl> <dt>object</dt>
<dd>an object of class PCA</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 Principal Component Analysis — predict.PCA

<dl>

 <dt>object</dt>
<dd>an object of class PCA</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.PCA: Predict projection for new rows with Principal Component Analysis

Description

Usage

Arguments

Author

See Also