Partial least squares regression with compositional response variables and covariates

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73610054" target="_blank" >RIV/61989592:15310/21:73610054 - isvavai.cz</a>

Result on the web

<a href="https://obd.upol.cz/id_publ/333189941" target="_blank" >https://obd.upol.cz/id_publ/333189941</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2020.1795813" target="_blank" >10.1080/02664763.2020.1795813</a>

Result language

angličtina

Original language name

Partial least squares regression with compositional response variables and covariates

Original language description

The common approach for regression analysis with compositional variables is to express compositions in log-ratio coordinates (coefficients) and then perform standard statistical processing in real space. Similar to working in real space, the problem is that the standard least squares regression fails when the number of parts of all compositional covariates is higher than the number of observations. The aim of this study is to analyze in detail the partial least squares (PLS) regression which can deal with this problem. In this paper, we focus on the PLS regression between more than one compositional response variable and more than one compositional covariate. First, we give the PLS regression model with log-ratio coordinates of compositional variables, then we express the PLS model directly in the simplex. We also prove that the PLS model is invariant under the change of coordinate system, such as the ilr coordinates with a different contrast matrix or the clr coefficients. Moreover, we give the estimation and inference for parameters in PLS model. Finally, the PLS model with clr coefficients is used to analyze the relationship between the chemical metabolites of Astragali Radix and the plasma metabolites of rat after giving Astragali Radix.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF APPLIED STATISTICS

ISSN

0266-4763

e-ISSN

—

Volume of the periodical

48

Issue of the periodical within the volume

16

Country of publishing house

GB - UNITED KINGDOM

Number of pages

20

Pages from-to

3130-3149

UT code for WoS article

000550960800001

EID of the result in the Scopus database

2-s2.0-85088389861