The latent budget model is a mixture model for compositional data sets in which the entries, a contingency table, may be either realizations from a product multinomial distribution or distribution free. Based on this model, the latent budget analysis considers the interactions of two variables; the ex planatory (row) and the response (column) variables. The package lba uses expectation-maximization and active constraints method (ACM) to carry out, respectively, the maximum likelihood and the least squares estimation of the model parameters. It contains three main functions, lba which performs the analysis, goodnessfit for model selection and goodness of fit and the plotting functions plotcorr and plotlba used as a help in the interpretation of the results.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-026.zip
lba, alabama, plotrix, scatterplot3d, rgl, MASS
Graphics, Multivariate, Psychometrics, Distributions, Econometrics, Environmetrics, NumericalMathematics, Optimization, Robust, SocialSciences, SpatioTemporal
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For attribution, please cite this work as
Jelihovschi & Allaman, "The R Journal: lba: An R Package for Latent Budget Analysis", The R Journal, 2018
BibTeX citation
@article{RJ-2018-026,
author = {Jelihovschi, Enio G. and Allaman, Ivan Bezerra},
title = {The R Journal: lba: An R Package for Latent Budget Analysis},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-026},
doi = {10.32614/RJ-2018-026},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {269-287}
}