Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes
The aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the adaptability and stability of three methodologies bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/11/11/2179 |
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| author | Moysés Nascimento Paulo Eduardo Teodoro Isabela de Castro Sant’Anna Laís Mayara Azevedo Barroso Ana Carolina Campana Nascimento Camila Ferreira Azevedo Larissa Pereira Ribeiro Teodoro Francisco José Correia Farias Helaine Claire Almeida Luiz Paulo de Carvalho |
| author_facet | Moysés Nascimento Paulo Eduardo Teodoro Isabela de Castro Sant’Anna Laís Mayara Azevedo Barroso Ana Carolina Campana Nascimento Camila Ferreira Azevedo Larissa Pereira Ribeiro Teodoro Francisco José Correia Farias Helaine Claire Almeida Luiz Paulo de Carvalho |
| author_sort | Moysés Nascimento |
| collection | DOAJ |
| description | The aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the adaptability and stability of three methodologies based on regression in the presence of influential points. Specifically, were evaluated methods based on simple, non-parametric and quantile (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula> = 0.50) regressions. The dataset used in this work corresponds to 18 variety trials of cotton cultivars that were conducted in the 2013–2014 and 2014–2015 crop seasons. The evaluated variable was the cotton fiber yield (kg/ha). Once we noticed that the effect of G × E interaction is significant, the statistical procedures adopted for the adaptability and stability analysis of the genotypes. To verify the presence of a possible influential point, we used the leverage values, studentized residuals (SR), DFBETAS and Cook’s distance. As a result, the influential points can modify the recommendation of genotypes, based on regression methods, in the presence of G × E interaction. The non-parametric and quantile (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula> = 0.50) regressions, which are based on median estimators, are less sensitive to the presence of influential points avoiding misleading recommendations of genotypes in terms of adaptability. |
| first_indexed | 2024-03-10T05:46:55Z |
| format | Article |
| id | doaj.art-3a6afbe41c404d4da8ccccd2debd5660 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T05:46:55Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-3a6afbe41c404d4da8ccccd2debd56602023-11-22T22:01:40ZengMDPI AGAgronomy2073-43952021-10-011111217910.3390/agronomy11112179Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton GenotypesMoysés Nascimento0Paulo Eduardo Teodoro1Isabela de Castro Sant’Anna2Laís Mayara Azevedo Barroso3Ana Carolina Campana Nascimento4Camila Ferreira Azevedo5Larissa Pereira Ribeiro Teodoro6Francisco José Correia Farias7Helaine Claire Almeida8Luiz Paulo de Carvalho9Department of Statistics, Federal University of Viçosa, Viçosa 36570-977, BrazilDepartment of Agronomy, Campus Chapadão do Sul, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, BrazilCenter of Agroforestry Systems and Ruber, Agronomic Institute of Campinas, Campinas 13020-902, BrazilDepartment of Mathematics and Statistics, Federal University of Rondônia, Ji-Paraná 76900-730, BrazilDepartment of Statistics, Federal University of Viçosa, Viçosa 36570-977, BrazilDepartment of Statistics, Federal University of Viçosa, Viçosa 36570-977, BrazilDepartment of Agronomy, Campus Chapadão do Sul, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, BrazilNational Center for Cotton Research, Brazilian Agricultural Research Corporation, Campina Grande 58428-095, BrazilDepartment of Statistics, Federal University of Viçosa, Viçosa 36570-977, BrazilNational Center for Cotton Research, Brazilian Agricultural Research Corporation, Campina Grande 58428-095, BrazilThe aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the adaptability and stability of three methodologies based on regression in the presence of influential points. Specifically, were evaluated methods based on simple, non-parametric and quantile (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula> = 0.50) regressions. The dataset used in this work corresponds to 18 variety trials of cotton cultivars that were conducted in the 2013–2014 and 2014–2015 crop seasons. The evaluated variable was the cotton fiber yield (kg/ha). Once we noticed that the effect of G × E interaction is significant, the statistical procedures adopted for the adaptability and stability analysis of the genotypes. To verify the presence of a possible influential point, we used the leverage values, studentized residuals (SR), DFBETAS and Cook’s distance. As a result, the influential points can modify the recommendation of genotypes, based on regression methods, in the presence of G × E interaction. The non-parametric and quantile (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula> = 0.50) regressions, which are based on median estimators, are less sensitive to the presence of influential points avoiding misleading recommendations of genotypes in terms of adaptability.https://www.mdpi.com/2073-4395/11/11/2179linear regressionquantile regressionnon-parametric regressiongenotype × environmental interaction |
| spellingShingle | Moysés Nascimento Paulo Eduardo Teodoro Isabela de Castro Sant’Anna Laís Mayara Azevedo Barroso Ana Carolina Campana Nascimento Camila Ferreira Azevedo Larissa Pereira Ribeiro Teodoro Francisco José Correia Farias Helaine Claire Almeida Luiz Paulo de Carvalho Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes Agronomy linear regression quantile regression non-parametric regression genotype × environmental interaction |
| title | Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes |
| title_full | Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes |
| title_fullStr | Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes |
| title_full_unstemmed | Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes |
| title_short | Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes |
| title_sort | influential points in adaptability and stability methods based on regression models in cotton genotypes |
| topic | linear regression quantile regression non-parametric regression genotype × environmental interaction |
| url | https://www.mdpi.com/2073-4395/11/11/2179 |
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