Likelihood ratio test between two groups of castor oil plant traits

ABSTRACT: The likelihood ratio test (LRT), to the independence between two sets of variables, allows to identify whether there is a dependency relationship between them. The aim of this study was to calculate the type I error and power of the LRT for determining independence between two sets of vari...

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Main Authors: Betania Brum, Sidinei José Lopes, Daniel Furtado Ferreira, Lindolfo Storck, Alberto Cargnelutti Filho
Format: Article
Language:English
Published: Universidade Federal de Santa Maria 2016-01-01
Series:Ciência Rural
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782016005007104&lng=en&tlng=en
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spelling doaj-d6c8faaec0d0490c914753fe1a1933a32020-11-24T23:17:45ZengUniversidade Federal de Santa MariaCiência Rural1678-45962016-01-01010.1590/0103-8478cr20151418S0103-84782016005007104Likelihood ratio test between two groups of castor oil plant traitsBetania BrumSidinei José LopesDaniel Furtado FerreiraLindolfo StorckAlberto Cargnelutti FilhoABSTRACT: The likelihood ratio test (LRT), to the independence between two sets of variables, allows to identify whether there is a dependency relationship between them. The aim of this study was to calculate the type I error and power of the LRT for determining independence between two sets of variables under multivariate normal distributions in scenarios consisting of combinations of 16 sample sizes; 40 combinations of the number of variables of the two groups; and nine degrees of correlation between the variables (for the power). The rate of type I error and power were calculate at 640 and 5,760 scenarios, respectively. A performance evaluation of the LRT was conducted by computer simulation by the Monte Carlo method, using 2,000 simulations in each scenario. When the number of variables was large (24), the TRV controlled the rate of type I errors and showed high power in sizes greater than 100 samples. For small sample sizes (25, 30 and 50), the test showed good performance because the number of variables did not exceed 12.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782016005007104&lng=en&tlng=enRicinus communis L.Erro tipo IPoder do testedistribuição normal multivariada.
collection DOAJ
language English
format Article
sources DOAJ
author Betania Brum
Sidinei José Lopes
Daniel Furtado Ferreira
Lindolfo Storck
Alberto Cargnelutti Filho
spellingShingle Betania Brum
Sidinei José Lopes
Daniel Furtado Ferreira
Lindolfo Storck
Alberto Cargnelutti Filho
Likelihood ratio test between two groups of castor oil plant traits
Ciência Rural
Ricinus communis L.
Erro tipo I
Poder do teste
distribuição normal multivariada.
author_facet Betania Brum
Sidinei José Lopes
Daniel Furtado Ferreira
Lindolfo Storck
Alberto Cargnelutti Filho
author_sort Betania Brum
title Likelihood ratio test between two groups of castor oil plant traits
title_short Likelihood ratio test between two groups of castor oil plant traits
title_full Likelihood ratio test between two groups of castor oil plant traits
title_fullStr Likelihood ratio test between two groups of castor oil plant traits
title_full_unstemmed Likelihood ratio test between two groups of castor oil plant traits
title_sort likelihood ratio test between two groups of castor oil plant traits
publisher Universidade Federal de Santa Maria
series Ciência Rural
issn 1678-4596
publishDate 2016-01-01
description ABSTRACT: The likelihood ratio test (LRT), to the independence between two sets of variables, allows to identify whether there is a dependency relationship between them. The aim of this study was to calculate the type I error and power of the LRT for determining independence between two sets of variables under multivariate normal distributions in scenarios consisting of combinations of 16 sample sizes; 40 combinations of the number of variables of the two groups; and nine degrees of correlation between the variables (for the power). The rate of type I error and power were calculate at 640 and 5,760 scenarios, respectively. A performance evaluation of the LRT was conducted by computer simulation by the Monte Carlo method, using 2,000 simulations in each scenario. When the number of variables was large (24), the TRV controlled the rate of type I errors and showed high power in sizes greater than 100 samples. For small sample sizes (25, 30 and 50), the test showed good performance because the number of variables did not exceed 12.
topic Ricinus communis L.
Erro tipo I
Poder do teste
distribuição normal multivariada.
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782016005007104&lng=en&tlng=en
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