Números de ensaios, observações e variáveis na correlação canônica em centeio
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/26030 |
Resumo: | Multivariate techniques are important analysis tools for understanding the object of study. For greater reliability in the interpretations of models, the correct sample size is necessary. Little has been explored about the number of measurements or tests needed in the diagnosis of multicollinearity and the correlation of the first canonical pair and the relationship between the number of observations and variables for estimating the correlation in the first canonical pair. Thus, the objectives of this study were to determine: the number of trials necessary to estimate the degree of multicollinearity by the condition number (NC) and variance inflation factor (FIV) indicators and the estimate of the correlation of the first canonical pair; and the relationship between the number of observations and variables for estimating the canonical correlation. Eight uniformity tests were conducted with two rye cultivars, with the evaluation of morphological and productive characters. To study the number of tests for the diagnosis of the degree of multicollinearity in each group of variables, cases were planned – different combinations of characters – and the diagnosis of multicollinearity was performed using the NC and FIV indicators. The repeatability analysis and the number of trials to estimate multicollinearity were performed by five methods in each case, cultivar and group of variables. In determining the number of trials for estimating the correlation of the first canonical pair, scenarios were considered, consisting of cases with the same number of characters combined in each group of variables. For each scenario and cultivar, repeatability analysis was performed by five methods: analysis of variance, principal components and structural analysis, based on correlation and variance and covariance matrices. Then, the number of trials for different levels of accuracy was determined. To study the relationship between the number of observations and variables (n:p) to estimate the correlation of the first canonical pair, 1,000 samples with multivariate normal distribution were simulated in 100 sample sizes (n:p = 1, 2, 3, . .., 100), three sowing dates and two cultivars. Then, with the averages' estimates in each planned sample size, was determined the n:p relationship through two segmented regression models with plateau. There is need of three and 16 trials for the diagnosis of multicollinearity and of four and eight trials for estimating the first pair correlation canonical in the cultivars BRS Progresso and Temprano, respectively, with a minimum accuracy of 95%. While, in canonical correlation analysis, it is recommended the use at least 27 observations for each variable. |
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Números de ensaios, observações e variáveis na correlação canônica em centeioNumbers of trials, observations and variables in the canonical correlation in ryeSecale cereale L.Análise multivariadaMulticolinearidadeDimensionamento amostralMultivariate analysisMulticollinearitySample sizingCNPQ::CIENCIAS AGRARIAS::AGRONOMIAMultivariate techniques are important analysis tools for understanding the object of study. For greater reliability in the interpretations of models, the correct sample size is necessary. Little has been explored about the number of measurements or tests needed in the diagnosis of multicollinearity and the correlation of the first canonical pair and the relationship between the number of observations and variables for estimating the correlation in the first canonical pair. Thus, the objectives of this study were to determine: the number of trials necessary to estimate the degree of multicollinearity by the condition number (NC) and variance inflation factor (FIV) indicators and the estimate of the correlation of the first canonical pair; and the relationship between the number of observations and variables for estimating the canonical correlation. Eight uniformity tests were conducted with two rye cultivars, with the evaluation of morphological and productive characters. To study the number of tests for the diagnosis of the degree of multicollinearity in each group of variables, cases were planned – different combinations of characters – and the diagnosis of multicollinearity was performed using the NC and FIV indicators. The repeatability analysis and the number of trials to estimate multicollinearity were performed by five methods in each case, cultivar and group of variables. In determining the number of trials for estimating the correlation of the first canonical pair, scenarios were considered, consisting of cases with the same number of characters combined in each group of variables. For each scenario and cultivar, repeatability analysis was performed by five methods: analysis of variance, principal components and structural analysis, based on correlation and variance and covariance matrices. Then, the number of trials for different levels of accuracy was determined. To study the relationship between the number of observations and variables (n:p) to estimate the correlation of the first canonical pair, 1,000 samples with multivariate normal distribution were simulated in 100 sample sizes (n:p = 1, 2, 3, . .., 100), three sowing dates and two cultivars. Then, with the averages' estimates in each planned sample size, was determined the n:p relationship through two segmented regression models with plateau. There is need of three and 16 trials for the diagnosis of multicollinearity and of four and eight trials for estimating the first pair correlation canonical in the cultivars BRS Progresso and Temprano, respectively, with a minimum accuracy of 95%. While, in canonical correlation analysis, it is recommended the use at least 27 observations for each variable.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESAs técnicas multivariadas são ferramentas importantes de análise na compreensão do objeto de estudo. Para maior confiabilidade nas interpretações de modelos é necessário o correto dimensionamento amostral. Pouco tem sido explorado sobre o número de medições ou ensaios necessários no diagnóstico de multicolinearidade e na correlação do primeiro par canônico e a relação entre o número de observações e variáveis para a estimativa da correlação no primeiro par canônico. Assim, os objetivos deste estudo foram determinar: o número de ensaios necessário para estimar o grau de multicolinearidade pelos indicadores número de condição (NC) e fator de inflação da variância (FIV) e a estimativa da correlação do primeiro par canônico; e a relação entre o número de observações e de variáveis para a estimativa da correlação canônica. Foram conduzidos oito ensaios de uniformidade com duas cultivares de centeio, com a avaliação de caracteres morfológicos e produtivos. Para o estudo do número de ensaios para o diagnóstico do grau de multicolinearidade em cada grupo de variáveis, foram planejados casos – diferentes combinações de caracteres – e realizado o diagnóstico de multicolinearidade pelos indicadores NC e FIV. A análise de repetibilidade e o número de ensaios para estimar a multicolinearidade foi realizada por cinco métodos em cada caso, cultivar e grupo de variáveis. Na determinação do número de ensaio para a estimação da correlação do primeiro par canônico, foram considerados cenários, constituídos por casos com o mesmo número de caracteres combinados em cada grupo de variáveis. Para cada cenário e cultivar, foi realizada a análise de repetibilidade por cinco métodos: análise de variância, componentes principais e análise estrutural, com base nas matrizes de correlação e de variâncias e covariâncias. Em seguida, foi determinado o número de ensaios para diferentes níveis de precisão. Para estudar a relação entre o número de observações e variáveis (n:p) para estimar a correlação do primeiro par canônico, 1.000 amostras com distribuição normal multivariada foram simuladas em 100 tamanhos de amostra (n:p = 1, 2, 3, ..., 100), três épocas de semeadura e duas cultivares. Em seguida, com as estimativas de médias em cada tamanho de amostra planejado, foi determinada a relação n:p por meio de dois modelos de regressão segmentados com platô. Há a necessidade de três e 16 ensaios para o diagnóstico de multicolinearidade e quatro e oito ensaios para estimar a correlação do primeiro par canônico nas cultivares BRS Progresso e Temprano, respectivamente, com precisão mínima de 95%. Enquanto que, em análise de correlação canônica, é recomendado, a utilização de no mínimo de 27 observações para cada variável.Universidade Federal de Santa MariaBrasilAgronomiaUFSMPrograma de Pós-Graduação em AgronomiaCentro de Ciências RuraisCargnelutti Filho, Albertohttp://lattes.cnpq.br/0233728865094243Lucio, Alessandro Dal'ColBortolli, Betânia Brum deHaesbaert, Fernando MachadoToebe, MarcosNeu, Ismael Mario Marcio2022-08-31T17:46:11Z2022-08-31T17:46:11Z2022-02-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/26030porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2022-08-31T17:46:11Zoai:repositorio.ufsm.br:1/26030Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2022-08-31T17:46:11Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Números de ensaios, observações e variáveis na correlação canônica em centeio Numbers of trials, observations and variables in the canonical correlation in rye |
title |
Números de ensaios, observações e variáveis na correlação canônica em centeio |
spellingShingle |
Números de ensaios, observações e variáveis na correlação canônica em centeio Neu, Ismael Mario Marcio Secale cereale L. Análise multivariada Multicolinearidade Dimensionamento amostral Multivariate analysis Multicollinearity Sample sizing CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Números de ensaios, observações e variáveis na correlação canônica em centeio |
title_full |
Números de ensaios, observações e variáveis na correlação canônica em centeio |
title_fullStr |
Números de ensaios, observações e variáveis na correlação canônica em centeio |
title_full_unstemmed |
Números de ensaios, observações e variáveis na correlação canônica em centeio |
title_sort |
Números de ensaios, observações e variáveis na correlação canônica em centeio |
author |
Neu, Ismael Mario Marcio |
author_facet |
Neu, Ismael Mario Marcio |
author_role |
author |
dc.contributor.none.fl_str_mv |
Cargnelutti Filho, Alberto http://lattes.cnpq.br/0233728865094243 Lucio, Alessandro Dal'Col Bortolli, Betânia Brum de Haesbaert, Fernando Machado Toebe, Marcos |
dc.contributor.author.fl_str_mv |
Neu, Ismael Mario Marcio |
dc.subject.por.fl_str_mv |
Secale cereale L. Análise multivariada Multicolinearidade Dimensionamento amostral Multivariate analysis Multicollinearity Sample sizing CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
topic |
Secale cereale L. Análise multivariada Multicolinearidade Dimensionamento amostral Multivariate analysis Multicollinearity Sample sizing CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
Multivariate techniques are important analysis tools for understanding the object of study. For greater reliability in the interpretations of models, the correct sample size is necessary. Little has been explored about the number of measurements or tests needed in the diagnosis of multicollinearity and the correlation of the first canonical pair and the relationship between the number of observations and variables for estimating the correlation in the first canonical pair. Thus, the objectives of this study were to determine: the number of trials necessary to estimate the degree of multicollinearity by the condition number (NC) and variance inflation factor (FIV) indicators and the estimate of the correlation of the first canonical pair; and the relationship between the number of observations and variables for estimating the canonical correlation. Eight uniformity tests were conducted with two rye cultivars, with the evaluation of morphological and productive characters. To study the number of tests for the diagnosis of the degree of multicollinearity in each group of variables, cases were planned – different combinations of characters – and the diagnosis of multicollinearity was performed using the NC and FIV indicators. The repeatability analysis and the number of trials to estimate multicollinearity were performed by five methods in each case, cultivar and group of variables. In determining the number of trials for estimating the correlation of the first canonical pair, scenarios were considered, consisting of cases with the same number of characters combined in each group of variables. For each scenario and cultivar, repeatability analysis was performed by five methods: analysis of variance, principal components and structural analysis, based on correlation and variance and covariance matrices. Then, the number of trials for different levels of accuracy was determined. To study the relationship between the number of observations and variables (n:p) to estimate the correlation of the first canonical pair, 1,000 samples with multivariate normal distribution were simulated in 100 sample sizes (n:p = 1, 2, 3, . .., 100), three sowing dates and two cultivars. Then, with the averages' estimates in each planned sample size, was determined the n:p relationship through two segmented regression models with plateau. There is need of three and 16 trials for the diagnosis of multicollinearity and of four and eight trials for estimating the first pair correlation canonical in the cultivars BRS Progresso and Temprano, respectively, with a minimum accuracy of 95%. While, in canonical correlation analysis, it is recommended the use at least 27 observations for each variable. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-08-31T17:46:11Z 2022-08-31T17:46:11Z 2022-02-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/26030 |
url |
http://repositorio.ufsm.br/handle/1/26030 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Agronomia UFSM Programa de Pós-Graduação em Agronomia Centro de Ciências Rurais |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Agronomia UFSM Programa de Pós-Graduação em Agronomia Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
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1805922058823008256 |