Sample size for evaluation the of multicollinearity degree in productive traits of rye
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Publication Date: | 2022 |
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Format: | Article |
Language: | por |
Source: | Revista Ciência e Natura (Online) |
Download full: | https://periodicos.ufsm.br/cienciaenatura/article/view/41667 |
Summary: | 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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Sample size for evaluation the of multicollinearity degree in productive traits of ryeTamanho de amostra para avaliação do grau de multicolinearidade em caracteres produtivos de centeioSecale cereale LSampling designCorrelationMultivariate analysisSecale cereale LDimensionamento amostralCorrelaçãoAnálise multivariadaThe 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.Os objetivos deste trabalho foram determinar o tamanho de amostra (número de plantas) necessário para a estimação dos indicadores do grau de multicolinearidade - número de condição (NC), determinante da matriz de correlação (DET) e fator de inflação da variância (FIV) - em caracteres produtivos de centeio e verificar a variabilidade do tamanho de amostra entre os indicadores. Foram conduzidos cinco e três ensaios de uniformidade (cultivares BRS Progresso e Temprano, respectivamente) e avaliados sete caracteres produtivos em 780 plantas. Vinte e um casos foram obtidos a partir de sete caracteres, combinados cinco a cinco. Para cada caso, foram planejados 197 tamanhos de amostra (20, 25, 30, ..., 1.000 plantas) e para cada tamanho foram realizadas 2.000 reamostragens, com reposição. Para cada reamostra foram determinados o NC, DET e FIV e calculada a média das 2.000 estimativas de cada indicador do grau de multicolinearidade. Após, foi determinado o tamanho de amostra, por meio de: máxima curvatura modificado e modelos linear e quadrático segmentado com resposta platô. Houve superioridade do modelo quadrático segmentado com platô no ajuste do grau de multicolinearidade em função do tamanho de amostra para todos os indicadores. Há a necessidade de maior tamanho de amostra para a detecção da multicolinearidade quando o diagnóstico for realizado pelo DET e de tamanhos de amostra superiores a 101, 258 e 102 plantas quando o diagnóstico for realizado pelo número de condição, determinante e fator de inflação da variância, respectivamente.Universidade Federal de Santa Maria2022-01-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttps://periodicos.ufsm.br/cienciaenatura/article/view/4166710.5902/2179460X41667Ciência e Natura; Vol. 43 (2021): Continuous Publication; e38Ciência e Natura; v. 43 (2021): Publicação Contínua; e382179-460X0100-8307reponame:Revista Ciência e Natura (Online)instname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMporhttps://periodicos.ufsm.br/cienciaenatura/article/view/41667/pdfhttps://periodicos.ufsm.br/cienciaenatura/article/view/41667/htmlCopyright (c) 2021 Ciência e Naturainfo:eu-repo/semantics/openAccessNeu, Ismael Mario MárcioCargnelutti Filho, Albertode Bem, Cláudia MarquesKleinpaul, Jéssica AndiaraBandeira, Cirineu Tolfo2022-01-14T12:59:26Zoai:ojs.pkp.sfu.ca:article/41667Revistahttps://periodicos.ufsm.br/cienciaenatura/indexPUBhttps://periodicos.ufsm.br/cienciaenatura/oaicienciaenatura@ufsm.br || centraldeperiodicos@ufsm.br2179-460X0100-8307opendoar:2022-01-14T12:59:26Revista Ciência e Natura (Online) - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Sample size for evaluation the of multicollinearity degree in productive traits of rye Tamanho de amostra para avaliação do grau de multicolinearidade em caracteres produtivos de centeio |
title |
Sample size for evaluation the of multicollinearity degree in productive traits of rye |
spellingShingle |
Sample size for evaluation the of multicollinearity degree in productive traits of rye Neu, Ismael Mario Márcio Secale cereale L Sampling design Correlation Multivariate analysis Secale cereale L Dimensionamento amostral Correlação Análise multivariada |
title_short |
Sample size for evaluation the of multicollinearity degree in productive traits of rye |
title_full |
Sample size for evaluation the of multicollinearity degree in productive traits of rye |
title_fullStr |
Sample size for evaluation the of multicollinearity degree in productive traits of rye |
title_full_unstemmed |
Sample size for evaluation the of multicollinearity degree in productive traits of rye |
title_sort |
Sample size for evaluation the of multicollinearity degree in productive traits of rye |
author |
Neu, Ismael Mario Márcio |
author_facet |
Neu, Ismael Mario Márcio Cargnelutti Filho, Alberto de Bem, Cláudia Marques Kleinpaul, Jéssica Andiara Bandeira, Cirineu Tolfo |
author_role |
author |
author2 |
Cargnelutti Filho, Alberto de Bem, Cláudia Marques Kleinpaul, Jéssica Andiara Bandeira, Cirineu Tolfo |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Neu, Ismael Mario Márcio Cargnelutti Filho, Alberto de Bem, Cláudia Marques Kleinpaul, Jéssica Andiara Bandeira, Cirineu Tolfo |
dc.subject.por.fl_str_mv |
Secale cereale L Sampling design Correlation Multivariate analysis Secale cereale L Dimensionamento amostral Correlação Análise multivariada |
topic |
Secale cereale L Sampling design Correlation Multivariate analysis Secale cereale L Dimensionamento amostral Correlação Análise multivariada |
description |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-14 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufsm.br/cienciaenatura/article/view/41667 10.5902/2179460X41667 |
url |
https://periodicos.ufsm.br/cienciaenatura/article/view/41667 |
identifier_str_mv |
10.5902/2179460X41667 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.ufsm.br/cienciaenatura/article/view/41667/pdf https://periodicos.ufsm.br/cienciaenatura/article/view/41667/html |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Ciência e Natura info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Ciência e Natura |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência e Natura; Vol. 43 (2021): Continuous Publication; e38 Ciência e Natura; v. 43 (2021): Publicação Contínua; e38 2179-460X 0100-8307 reponame:Revista Ciência e Natura (Online) instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Revista Ciência e Natura (Online) |
collection |
Revista Ciência e Natura (Online) |
repository.name.fl_str_mv |
Revista Ciência e Natura (Online) - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
cienciaenatura@ufsm.br || centraldeperiodicos@ufsm.br |
_version_ |
1839277885882368000 |