mfa: Multiple Factor Analysis - MFA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Multiple Factor Analysis - MFA

Description

This is a wrapper for the FactoMineR::MFA function for computing MFA.

Usage

mfa(X, type = rep("c", length(X)), graph = FALSE, ...)

Arguments

X	list of input blocks.
type	character vector indicating block types, defaults to rep("c", length(X)) for continuous values.
graph	logical indicating if decomposition should be plotted.
...	additional arguments for RGCCA approach.

Details

MFA is a methods typically used to compare several equally sized matrices. It is often used in sensory analyses, where matrices consist of sensory characteristics and products, and each assessor generates one matrix each. In its basic form, MFA scales all matrices by their largest eigenvalue, concatenates them and performs PCA on the result. There are several possibilities for plots and inspections of the model, handling of categorical and continuous inputs etc. connected to MFA.

Value

multiblock object including relevant scores and loadings. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

References

Pagès, J. (2005). Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire valley. Food Quality and Preference, 16(7), 642–649.

See Also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex. Common functions for computation and extraction of results and plotting are found in multiblock_results and multiblock_plots, respectively.

Examples

data(potato)
potList <- as.list(potato[c(1,2,9)])
pot.mfa    <- mfa(potList)
if(interactive()){
  plot(pot.mfa$MFA)
}

multiblock documentation built on Nov. 18, 2023, 5:06 p.m.