The FactoMineR is a package that includes qualitative and quantitative variables. The observations and supplementary variables are also included in these packages.
FactoMineR is an R package designed for exploratory multivariate data analysis and dimensionality reduction techniques, particularly focused on methods related to factor analysis and principal component analysis (PCA). It provides a wide range of functionalities to analyze and interpret complex data sets, especially those with a large number of variables.
Here are some key aspects and features of FactoMineR:
Principal Component Analysis (PCA): FactoMineR allows you to perform PCA on your data, which is a technique used for reducing the dimensionality of the data while retaining as much variance as possible. This helps in visualizing and understanding the underlying structure of the data.
library(FactoMineR)
# Example of PCA
data <- data.frame(matrix(rnorm(1000), ncol = 10))
result <- PCA(data)
summary(result)
Factor Analysis: FactoMineR also supports factor analysis, a statistical method used to identify underlying latent factors that explain patterns of correlations among observed variables.
library(FactoMineR)
# Example of Factor Analysis
data <- data.frame(matrix(rnorm(1000), ncol = 10))
result <- PCA(data, method = “Factor”)
summary(result)
Multiple Correspondence Analysis (MCA): MCA is a technique for analyzing contingency tables with more than two categorical variables. FactoMineR provides functions for conducting MCA.
library(FactoMineR)
# Example of MCA
data <- data.frame(matrix(sample(1:5, 100, replace = TRUE), ncol = 10))
result <- MCA(data)
summary(result)
Visualization: FactoMineR comes with various plotting functions to visualize the results of the analyses, such as scatter plots, biplots, and scree plots.
# Example of scatter plot
plot(result, choix = “ind”)
These are just basic examples, and FactoMineR offers more advanced options for customization and interpretation. It’s a powerful tool for researchers and data analysts working on multivariate data analysis tasks in R.