gpa: Generalized Procrustes Analysis - GPA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Generalized Procrustes Analysis - GPA

Description

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

Usage

gpa(X, graph = FALSE, ...)

Arguments

X	list of input blocks.
graph	logical indicating if decomposition should be plotted.
...	additional arguments for RGCCA approach.

Details

GPA is a generalisation of Procrustes analysis, where one matrix is scaled and rotated to be as similar as possible to another one. Through the generalisation, individual scaling and rotation of each input matrix is performed against a common representation which is estimated in an iterative manner.

Value

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

References

Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika. 40: 33–51.

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.gpa    <- gpa(potList)
plot(scores(pot.gpa), labels="names")

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