Sample size for evaluation the of multicollinearity degree in productive traits of rye

Authors

DOI:

https://doi.org/10.5902/2179460X41667

Keywords:

Secale cereale L, Sampling design, Correlation, Multivariate analysis

Abstract

The objectives of this work were to determine the sample size (number of plants) necessary to estimate the indicators of the of multicollinearity degree - condition number (CN), determinant of the correlation matrix (DET), and variance inflation factor (VIF) - in productive traits of rye and to verify the variability of the sample size between the indicators. Five and three uniformity trials were conducted with the cultivars BRS Progresso and Temprano, respectively, and seven productive traits were evaluated in 780 plants. Twenty-one cases were obtained from seven traits, combined five to five. In each case, 197 sample sizes were planned (20, 25, 30, ..., 1,000 plants) and in each size 2,000 resampling were performed, with replacement. For each resample the CN, DET and FIV were determined and the average among 2,000 estimates of each indicator of the multicollinearity degree was calculated. Then, for each case and indicator, the sample size was determined through three models: models of maximum modified curvature, segmented linear with plateau response, and segmented quadratic with plateau response. There was superiority the quadratic model segmented with plateau in adjusting the degree of multicollinearity according to the sample size for all indicators. There is a need greater sample size to detect multicollinearity when diagnosed by DET and for sizes larger than 101, 258 and 102 plants when diagnosing for the number of conditions, determinant and inflation factor performed, respectively.

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Author Biographies

Ismael Mario Márcio Neu, Universidade Federal de Santa Maria, Centro de Ciências Rurais, Programa de Pós-Graduação em Agronomia, Santa Maria, RS

Cursou o Técnico Agrícola em nível pós-médio com Habilitação em Agropecuária no Colégio Politécnico da UFSM. Tem mestrado em Agronomia, área de abrangência em Fitotecnia. Atualmente, é doutorando no Programa de Pós Graduação em Agronomia, área de abrangência em Fitotecnia pela Universidade Federal de Santa Maria.

Alberto Cargnelutti Filho, Universidade Federal de Santa Maria, Centro de Ciências Rurais, Programa de Pós-Graduação em Agronomia, Santa Maria, RS

Possui graduação, mestrado e doutorado em Agronomia. Atualmente é Professor Associado III do Departamento de Fitotecnia da Universidade Federal de Santa Maria. Atua na área de Estatística Experimental, com ênfase em Técnicas Experimentais, Planejamento de Experimentos, Biometria aplicada ao Melhoramento Genética Vegetal e Amostragem, atuando principalmente nos seguintes temas: planejamento experimental, precisão experimental, modelos biométricos, modelos de crescimento, amostragem, estatística aplicada, avaliação de cultivares.

Cláudia Marques de Bem, Universidade Federal de Santa Maria, Centro de Ciências Rurais, Programa de Pós-Graduação em Agronomia, Santa Maria, RS

Possui graduação e Mestrado em Zootecnia e Doutorado em Agronomia. Especialista em Estatística e Modelagem Quantitativa e Pós-doutorado em Agronomia.

Jéssica Andiara Kleinpaul, Universidade Federal de Santa Maria, Centro de Ciências Rurais, Programa de Pós-Graduação em Agronomia, Santa Maria, RS

Possui Técnico em Paisagismo e graduação e Mestrado em Agronomia. Atualmente é doutoranda em Agronomia, na Universidade Federal de Santa Maria.

Cirineu Tolfo Bandeira, Universidade Federal de Santa Maria, Centro de Ciências Rurais, Programa de Pós-Graduação em Agronomia, Santa Maria, RS

Possui Técnico Agropecuária e Graduação e Mestrado em Agronomia. Atualmente é Engenheiro Agrônomo da Cotripal Agropecuária Cooperativa.

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Published

2021-03-10 — Updated on 2022-01-14

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How to Cite

Neu, I. M. M., Cargnelutti Filho, A., de Bem, C. M., Kleinpaul, J. A., & Bandeira, C. T. (2022). Sample size for evaluation the of multicollinearity degree in productive traits of rye. Ciência E Natura, 43, e38. https://doi.org/10.5902/2179460X41667 (Original work published March 10, 2021)

Issue

Vol. 43 (2021): Continuous Publication

Section

Statistics

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