zCompositions-package: Treatment of Zeros, Left-Censored and Missing Values in...

In Japal/zCompositions: Treatment of Zeros, Left-Censored and Missing Values in Compositional Data Sets

Treatment of Zeros, Left-Censored and Missing Values in Compositional Data Sets

Description

Following compositional data analysis principles, this package provides simple and friendly tools to explore and impute zeros, left-censored (such as rounded zeros or values below single or multiple limits of detection; a.k.a nondetects) and missing data; including zero pattern/group-wise data analysis and testing procedures.

Details

Package:	zCompositions
Type:	Package
Version:	1.5.0-3
Date:	2024-03-13
License:	GPL (>= 2)

Author(s)

Javier Palarea-Albaladejo and Josep Antoni Martin-Fernandez

Maintainer: Javier Palarea-Albaladejo <[e-mail]>

References

Martin-Fernandez, J.A., Barcelo-Vidal, C., Pawlowsky-Glahn, V., 2003. Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Mathematical Geology 35 (3): 253-27.

Martin-Fernandez, J.A., Hron, K., Templ, M., Filzmoser, P., Palarea-Albaladejo, J., 2012. Model-based replacement of rounded zeros in compositional data: Classical and robust approaches. Computational Statistics and Data Analysis 56: 2688-2704.

Martin-Fernandez, J.A., Hron, K., Templ, M., Filzmoser, P., Palarea-Albaladejo, J., 2015. Bayesian-multiplicative treatment of count zeros in compositional data sets. Statistical Modelling 15 (2): 134-158.

Palarea-Albaladejo, J., Martin-Fernandez, J.A., Gomez-Garcia, J., 2007. A parametric approach for dealing with compositional rounded zeros. Mathematical Geology 39 (7): 625-645.

Palarea-Albaladejo, J., Martin-Fernandez, J.A., 2008. A modified EM alr-algorithm for replacing rounded zeros in compositional data sets. Computers & Geosciences 34 (8): 902-917.

Palarea-Albaladejo, J., Martin-Fernandez, J.A., 2013. Values below detection limit in compositional chemical data. Analytica Chimica Acta 764: 32-43.

Palarea-Albaladejo, J., Martin-Fernandez J.A., Olea, R.A., 2014. A bootstrap estimation scheme for chemical compositional data with nondetects. Journal of Chemometrics 28: 585-599.

Palarea-Albaladejo J. and Martin-Fernandez J.A., 2015. zCompositions – R package for multivariate imputation of left-censored data under a compositional approach. Chemometrics and Intelligent Laboratory Systems 143: 85-96.

Palarea-Albaladejo, J, Antoni Martín-Fernández, J, Ruiz-Gazen, A, Thomas-Agnan, C., 2022. lrSVD: An efficient imputation algorithm for incomplete high-throughput compositional data. Journal of Chemometrics 36: e3459.

See Also

Aitchison, J., 1986. The Statistical Analysis of Compositional Data. Monographs on Statistics and Applied Probability. Chapman and Hall Ltd., London, UK (re-edited in 2003 with additional material).

Filzmoser, P., Hron, K., Templ, M., 2018. Applied Compositional Data Analysis. With Worked Examples in R. Springer, Switzerland.

Filzmoser P., Hron K., Martín-Fernández J.A., Palarea-Albaladejo J. (eds.), 2021. Advances in Compositional Data Analysis. Springer, Cham.

Pawlowsky-Glahn, V., Buccianti, A. (Eds.), 2011. Compositional Data Analysis: Theory and Applications. John Wiley & Sons, Ltd., Chichester,UK.

Pawlowsky-Glahn, V., Egozcue, J.J., Tolosana-Delgado, R., 2015. Modeling and analysis of compositional data. John Wiley & Sons, Ltd., Chichester, UK.

van den Boogaart, K.G., Tolosana-Delgado, R., 2013, Analyzing Compositional Data with R. Springer-Verlag, Berlin, Germany.

Japal/zCompositions documentation built on March 17, 2024, 5:19 p.m.