PCA for Categorical Variables in R | R-bloggers

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PCA for Categorical Variables in R
Posted on November 20, 2022 by finnstats in R bloggers | 0 Comments
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PCA for Categorical Variables in R, Using Principal Component Analysis to minimize the dimensionality of your data frame may have crossed your mind (PCA).
However, can PCA be applied to a data set with categorical variables?
You’ll discover how to apply Principal Component Analysis (PCA) to data frames that include categorical variables in this course.
Additionally, you’ll discover how to use the R programming language to put these alternatives into practice.
Can a Data Frame with Categorical Variables be Used for PCA?
The answer is not straightforward: although it is technically possible to run a PCA on a data frame containing categorical variables, this doesn’t appear to be the best course of action.
The primary explanation for this is that the PCA, which involves dissecting the variance structure of the variable, is made to function better with numerical or continuous variables.
PCA won’t be effective with categorical variables since they lack a variance structure (they are not numerical).
Converting categorical variables into a sequence of binary variables with 0 and 1 values is one way to do the PCA in a data set with categorical variables.
However, this definitely wouldn’t make sense if we had a data set with only binary variables; instead, we should look at other options if we want to study a data set including categorical data.
Extension Libraries
We’ll need the FactoMineR, vcd, and factoextra packages for this tutorial. The following code can be used to install these packages if necessary:
install.packages("FactoMineR") install.packages("vcd") install.packages("factoextra")
library(FactoMineR) library(vcd) library(factoextra)
Factorial Analysis of Mixed Data (FAMD) Is a PCA for Categorical Variables Alternate
A major component method is the Factor Analysis of Mixed Data (FAMD). By considering several sorts of data, this approach enables one to examine how similar people are.
This technique consists of two steps: first, it suitably encodes the data; and second, it searches the data set iteratively for the K principal components.
Similar to how PCA operates, this main component search does the same.
Variables that are both quantitative and qualitative are standardized during the Factorial Analysis of Mixed Data. This balances the impact of each group of variables.
An R programming language example of a factor analysis of mixed data (FAMD)
By using the FAMD() function from the FactoMineR package, we can construct this analysis and see how it functions using the R programming language.
Clustering Example Step-by-Step Methods in R » finnstats
We will use a portion of the wine data set from the FactoMineR software to illustrate this example:
data(wine) wine_data