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The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data

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The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data. / Paderewski, Jakub; Rodrigues, Paulo Canas.

In: AUSTRALIAN JOURNAL OF CROP SCIENCE, Vol. 8, No. 4, 2014, p. 640-645.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Paderewski, J & Rodrigues, PC 2014, 'The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data', AUSTRALIAN JOURNAL OF CROP SCIENCE, vol. 8, no. 4, pp. 640-645.

APA

Paderewski, J., & Rodrigues, P. C. (2014). The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data. AUSTRALIAN JOURNAL OF CROP SCIENCE, 8(4), 640-645.

Vancouver

Author

Paderewski, Jakub ; Rodrigues, Paulo Canas. / The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data. In: AUSTRALIAN JOURNAL OF CROP SCIENCE. 2014 ; Vol. 8, No. 4. pp. 640-645.

Bibtex - Download

@article{293df237223e4b7a9511e3985348a612,
title = "The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data",
abstract = "The study of genotype-by-environment interaction (GEI) is of key importance in plant sciences because an understanding of this allows a great improvement in complex phenotypic traits. Genotypes and environments constitute a two-way factorial design. The phenotypic data for these studies, usually arranged in two-way data tables with genotypes and environments (location-year combinations). In plant breeding programs some genotypes are often discarded and others included from year to year, which results in the presence of missing values in these data sets. Several options are available for dealing with missing values in two-way data tables. One of the most widely used alternatives is the imputation of the missing cells using an expectation-maximization (EM) algorithm together with the additive main effects and multiplicative interaction (AMMI) model. In this paper we present a simulation study to investigate the influence of the pattern of missing values on the efficiency of the expectation-maximization AMMI (EM-AMMI) algorithm. Four scenarios are considered: one with cells missing completely at random; and three patterns with cells not missing at random (block-diagonal pattern, diagonal pattern and block-diagonal pattern with checks). The results are compared in terms of precision to estimate the missing cells and genotype selection.",
keywords = "Additive, Expectation maximization, Genotype-by-environment interaction, Main effects, Missing data, Multiplicative interaction model, Plant breeding programs, Winter wheat",
author = "Jakub Paderewski and Rodrigues, {Paulo Canas}",
year = "2014",
language = "English",
volume = "8",
pages = "640--645",
journal = "AUSTRALIAN JOURNAL OF CROP SCIENCE",
issn = "1835-2693",
publisher = "Southern Cross Publishing and Printing Pty Ltd",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data

AU - Paderewski, Jakub

AU - Rodrigues, Paulo Canas

PY - 2014

Y1 - 2014

N2 - The study of genotype-by-environment interaction (GEI) is of key importance in plant sciences because an understanding of this allows a great improvement in complex phenotypic traits. Genotypes and environments constitute a two-way factorial design. The phenotypic data for these studies, usually arranged in two-way data tables with genotypes and environments (location-year combinations). In plant breeding programs some genotypes are often discarded and others included from year to year, which results in the presence of missing values in these data sets. Several options are available for dealing with missing values in two-way data tables. One of the most widely used alternatives is the imputation of the missing cells using an expectation-maximization (EM) algorithm together with the additive main effects and multiplicative interaction (AMMI) model. In this paper we present a simulation study to investigate the influence of the pattern of missing values on the efficiency of the expectation-maximization AMMI (EM-AMMI) algorithm. Four scenarios are considered: one with cells missing completely at random; and three patterns with cells not missing at random (block-diagonal pattern, diagonal pattern and block-diagonal pattern with checks). The results are compared in terms of precision to estimate the missing cells and genotype selection.

AB - The study of genotype-by-environment interaction (GEI) is of key importance in plant sciences because an understanding of this allows a great improvement in complex phenotypic traits. Genotypes and environments constitute a two-way factorial design. The phenotypic data for these studies, usually arranged in two-way data tables with genotypes and environments (location-year combinations). In plant breeding programs some genotypes are often discarded and others included from year to year, which results in the presence of missing values in these data sets. Several options are available for dealing with missing values in two-way data tables. One of the most widely used alternatives is the imputation of the missing cells using an expectation-maximization (EM) algorithm together with the additive main effects and multiplicative interaction (AMMI) model. In this paper we present a simulation study to investigate the influence of the pattern of missing values on the efficiency of the expectation-maximization AMMI (EM-AMMI) algorithm. Four scenarios are considered: one with cells missing completely at random; and three patterns with cells not missing at random (block-diagonal pattern, diagonal pattern and block-diagonal pattern with checks). The results are compared in terms of precision to estimate the missing cells and genotype selection.

KW - Additive

KW - Expectation maximization

KW - Genotype-by-environment interaction

KW - Main effects

KW - Missing data

KW - Multiplicative interaction model

KW - Plant breeding programs

KW - Winter wheat

UR - http://www.scopus.com/inward/record.url?scp=84901197110&partnerID=8YFLogxK

M3 - Article

VL - 8

SP - 640

EP - 645

JO - AUSTRALIAN JOURNAL OF CROP SCIENCE

JF - AUSTRALIAN JOURNAL OF CROP SCIENCE

SN - 1835-2693

IS - 4

ER -