# TCA and TLRA: A comparison on contingency tables and compositional data

@article{Allard2020TCAAT, title={TCA and TLRA: A comparison on contingency tables and compositional data}, author={J. Allard and Shawn Champigny and Vartan Choulakian and S. Mahdi}, journal={arXiv: Methodology}, year={2020} }

There are two popular general approaches for the analysis and visualization of a contingency table and a compositional data set: Correspondence analysis (CA) and log ratio analysis (LRA). LRA includes two independently well developed methods: association models and compositional data analysis. The application of either CA or LRA to a contingency table or to compositional data set includes a preprocessing centering step. In CA the centering step is multiplicative, while in LRA it is log bi…

## 3 Citations

### Quantification of intrinsic quality of a principal dimension in correspondence analysis and taxicab correspondence analysis

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This essay considers CA and taxicab CA as a stepwise Hotelling/Tucker decomposition of the cross-covariance matrix of the row and column categories into four quadrants and reviews the notion of quality/quantity in multidimensional data analysis as discussed by Benzécri.

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Collins' (2002) statement "correspondence analysis makes you blind" followed after his seriation like description of a brand attribute count data set analyzed by Whitlark and Smith (2001), who…

### On the choice of weights in aggregate compositional data analysis

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- 2023

It is shown that in the aggregate case, the under-lying given data form a paired data sets composed of responses and qualitative covariates ; this fact helps to propose two approaches for analysis-visualization of data named log interaction of aggregates and aggregate of log interactions.

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