Multivariate nonnormality and multicollinearity in path analysis in corn
Autor(a) principal: | |
---|---|
Data de Publicação: | 2013 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Pesquisa Agropecuária Brasileira (Online) |
Texto Completo: | https://seer.sct.embrapa.br/index.php/pab/article/view/14132 |
Resumo: | The objective of this work was to evaluate the effect of multivariate nonnormality and multicollinearity in the path analysis of corn. We used data from 13 corn cultivar competition trials. The response variable (grain yield) and seven explanatory variables (number of days to tasseling, plant height, ear height, relative ear position, number of plants, number of ears and prolificity) were measured in each cultivar. Then, data transformation and the univariate and multivariate normality diagnosis were proceeded. The correlation coefficients were calculated and the diagnosis of multicollinearity was performed, before and after data transformation. The path analysis was done according to three methods: traditional; under multicollinearity (ridge path analysis); and traditional with variable elimination. Data transformation reduces the degree of multicollinearity and the variability of the direct effects, in the traditional path analysis with high multicollinearity. Multicollinearity exerts more impact on the estimation of the direct effects in path analysis than multivariate nonnormality. The traditional path analysis with elimination of variables is more appropriate than the ridge path analysis. |
id |
EMBRAPA-4_aadbfc3590cc2b6fdcd3d71008801ad6 |
---|---|
oai_identifier_str |
oai:ojs.seer.sct.embrapa.br:article/14132 |
network_acronym_str |
EMBRAPA-4 |
network_name_str |
Pesquisa Agropecuária Brasileira (Online) |
repository_id_str |
|
spelling |
Multivariate nonnormality and multicollinearity in path analysis in cornNão normalidade multivariada e multicolinearidade na análise de trilha em milhoZea mays; ridge analysis; elimination of variables; Box‑Cox transformationsZea mays; análise em crista; eliminação de variáveis; transformações Box‑CoxThe objective of this work was to evaluate the effect of multivariate nonnormality and multicollinearity in the path analysis of corn. We used data from 13 corn cultivar competition trials. The response variable (grain yield) and seven explanatory variables (number of days to tasseling, plant height, ear height, relative ear position, number of plants, number of ears and prolificity) were measured in each cultivar. Then, data transformation and the univariate and multivariate normality diagnosis were proceeded. The correlation coefficients were calculated and the diagnosis of multicollinearity was performed, before and after data transformation. The path analysis was done according to three methods: traditional; under multicollinearity (ridge path analysis); and traditional with variable elimination. Data transformation reduces the degree of multicollinearity and the variability of the direct effects, in the traditional path analysis with high multicollinearity. Multicollinearity exerts more impact on the estimation of the direct effects in path analysis than multivariate nonnormality. The traditional path analysis with elimination of variables is more appropriate than the ridge path analysis.O objetivo deste trabalho foi avaliar a interferência da não normalidade multivariada e da multicolinearidade na análise de trilha, em milho. Foram utilizados os dados de 13 ensaios de competição de cultivares de milho. Foram mensuradas a variável principal (produtividade de grãos) e sete variáveis explicativas (número de dias até o florescimento, estatura de plantas, altura de inserção da espiga, posição relativa da espiga, número de plantas, número de espigas e prolificidade), em cada cultivar. Procedeu-se, então, à transformação dos dados e ao diagnóstico de normalidade univariada e multivariada. Antes e após a transformação de dados, foram calculados os coeficientes de correlação e realizado o diagnóstico de multicolinearidade. A análise de trilha foi realizada por três métodos: tradicional; sob condições de multicolinearidade (análise de trilha em crista); e tradicional com eliminação de variáveis. A transformação de dados reduz o grau de multicolinearidade e a variabilidade das estimativas dos efeitos diretos, na análise de trilha tradicional com alto grau de multicolinearidade. A multicolinearidade exerce maior impacto sobre a estimativa dos efeitos diretos nas análises de trilha do que a não normalidade multivariada. A análise de trilha tradicional com eliminação de variáveis é mais adequada do que a análise de trilha em crista.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraCNPq e CAPESToebe, MarcosCargnelutti Filho, Alberto2013-07-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/14132Pesquisa Agropecuaria Brasileira; v.48, n.5, maio 2013; 466-477Pesquisa Agropecuária Brasileira; v.48, n.5, maio 2013; 466-4771678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://seer.sct.embrapa.br/index.php/pab/article/view/14132/11770https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/14132/9494info:eu-repo/semantics/openAccess2013-07-31T20:58:44Zoai:ojs.seer.sct.embrapa.br:article/14132Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2013-07-31T20:58:44Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Multivariate nonnormality and multicollinearity in path analysis in corn Não normalidade multivariada e multicolinearidade na análise de trilha em milho |
title |
Multivariate nonnormality and multicollinearity in path analysis in corn |
spellingShingle |
Multivariate nonnormality and multicollinearity in path analysis in corn Toebe, Marcos Zea mays; ridge analysis; elimination of variables; Box‑Cox transformations Zea mays; análise em crista; eliminação de variáveis; transformações Box‑Cox |
title_short |
Multivariate nonnormality and multicollinearity in path analysis in corn |
title_full |
Multivariate nonnormality and multicollinearity in path analysis in corn |
title_fullStr |
Multivariate nonnormality and multicollinearity in path analysis in corn |
title_full_unstemmed |
Multivariate nonnormality and multicollinearity in path analysis in corn |
title_sort |
Multivariate nonnormality and multicollinearity in path analysis in corn |
author |
Toebe, Marcos |
author_facet |
Toebe, Marcos Cargnelutti Filho, Alberto |
author_role |
author |
author2 |
Cargnelutti Filho, Alberto |
author2_role |
author |
dc.contributor.none.fl_str_mv |
CNPq e CAPES |
dc.contributor.author.fl_str_mv |
Toebe, Marcos Cargnelutti Filho, Alberto |
dc.subject.por.fl_str_mv |
Zea mays; ridge analysis; elimination of variables; Box‑Cox transformations Zea mays; análise em crista; eliminação de variáveis; transformações Box‑Cox |
topic |
Zea mays; ridge analysis; elimination of variables; Box‑Cox transformations Zea mays; análise em crista; eliminação de variáveis; transformações Box‑Cox |
description |
The objective of this work was to evaluate the effect of multivariate nonnormality and multicollinearity in the path analysis of corn. We used data from 13 corn cultivar competition trials. The response variable (grain yield) and seven explanatory variables (number of days to tasseling, plant height, ear height, relative ear position, number of plants, number of ears and prolificity) were measured in each cultivar. Then, data transformation and the univariate and multivariate normality diagnosis were proceeded. The correlation coefficients were calculated and the diagnosis of multicollinearity was performed, before and after data transformation. The path analysis was done according to three methods: traditional; under multicollinearity (ridge path analysis); and traditional with variable elimination. Data transformation reduces the degree of multicollinearity and the variability of the direct effects, in the traditional path analysis with high multicollinearity. Multicollinearity exerts more impact on the estimation of the direct effects in path analysis than multivariate nonnormality. The traditional path analysis with elimination of variables is more appropriate than the ridge path analysis. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-07-31 |
dc.type.none.fl_str_mv |
|
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://seer.sct.embrapa.br/index.php/pab/article/view/14132 |
url |
https://seer.sct.embrapa.br/index.php/pab/article/view/14132 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://seer.sct.embrapa.br/index.php/pab/article/view/14132/11770 https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/14132/9494 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira Pesquisa Agropecuária Brasileira |
publisher.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira Pesquisa Agropecuária Brasileira |
dc.source.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira; v.48, n.5, maio 2013; 466-477 Pesquisa Agropecuária Brasileira; v.48, n.5, maio 2013; 466-477 1678-3921 0100-104x reponame:Pesquisa Agropecuária Brasileira (Online) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Pesquisa Agropecuária Brasileira (Online) |
collection |
Pesquisa Agropecuária Brasileira (Online) |
repository.name.fl_str_mv |
Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
pab@sct.embrapa.br || sct.pab@embrapa.br |
_version_ |
1793416677798445056 |