Comparation of logistic regression methods and discrete choice model in the selection of habitats
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000300011 |
Resumo: | Based on a review of most recent data analyses on resource selection by animals as well as on recent suggestions that indicate the lack of an unified statistical theory that shows how resource selection can be detected and measured, the authors suggest that the concept of resource selection function (RSF) can be the base for the development of a theory. The revision of discrete choice models (DCM) is suggested as an approximation to estimate the RSF when the choice of animal or groups of animals involves different sets of available resource units. The definition of RSF requires that the resource which is being studied consists of discrete units. The statistical method often used to estimate the RSF is the logistic regression but DCM can also be used. The theory of DCM has been well developed for the analysis of data sets involving choices of products by humans, but it can also be applicable to the choice of habitat by animals, with some modifications. The comparison of the logistic regression with the DCM for one choice is made because the coefficient estimates of the logistic regression model include an intercept, which are not presented by the DCM. The objective of this work was to compare the estimates of the RSF obtained by applying the logistic regression and the DCM to the data set on habitat selection of the spotted owl (Strix occidentalis) in the north west of the United States. |
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network_name_str |
Scientia Agrícola (Online) |
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Comparation of logistic regression methods and discrete choice model in the selection of habitatsresource selectionmaximum likelihoodbinomial distributioncomparison testBased on a review of most recent data analyses on resource selection by animals as well as on recent suggestions that indicate the lack of an unified statistical theory that shows how resource selection can be detected and measured, the authors suggest that the concept of resource selection function (RSF) can be the base for the development of a theory. The revision of discrete choice models (DCM) is suggested as an approximation to estimate the RSF when the choice of animal or groups of animals involves different sets of available resource units. The definition of RSF requires that the resource which is being studied consists of discrete units. The statistical method often used to estimate the RSF is the logistic regression but DCM can also be used. The theory of DCM has been well developed for the analysis of data sets involving choices of products by humans, but it can also be applicable to the choice of habitat by animals, with some modifications. The comparison of the logistic regression with the DCM for one choice is made because the coefficient estimates of the logistic regression model include an intercept, which are not presented by the DCM. The objective of this work was to compare the estimates of the RSF obtained by applying the logistic regression and the DCM to the data set on habitat selection of the spotted owl (Strix occidentalis) in the north west of the United States.Escola Superior de Agricultura "Luiz de Queiroz"2010-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000300011Scientia Agricola v.67 n.3 2010reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162010000300011info:eu-repo/semantics/openAccessCardozo,Sandra VergaraManly,Bryan Frederick JohnDias,Carlos Tadeu dos Santoseng2010-06-18T00:00:00Zoai:scielo:S0103-90162010000300011Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2010-06-18T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
title |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
spellingShingle |
Comparation of logistic regression methods and discrete choice model in the selection of habitats Cardozo,Sandra Vergara resource selection maximum likelihood binomial distribution comparison test |
title_short |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
title_full |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
title_fullStr |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
title_full_unstemmed |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
title_sort |
Comparation of logistic regression methods and discrete choice model in the selection of habitats |
author |
Cardozo,Sandra Vergara |
author_facet |
Cardozo,Sandra Vergara Manly,Bryan Frederick John Dias,Carlos Tadeu dos Santos |
author_role |
author |
author2 |
Manly,Bryan Frederick John Dias,Carlos Tadeu dos Santos |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Cardozo,Sandra Vergara Manly,Bryan Frederick John Dias,Carlos Tadeu dos Santos |
dc.subject.por.fl_str_mv |
resource selection maximum likelihood binomial distribution comparison test |
topic |
resource selection maximum likelihood binomial distribution comparison test |
description |
Based on a review of most recent data analyses on resource selection by animals as well as on recent suggestions that indicate the lack of an unified statistical theory that shows how resource selection can be detected and measured, the authors suggest that the concept of resource selection function (RSF) can be the base for the development of a theory. The revision of discrete choice models (DCM) is suggested as an approximation to estimate the RSF when the choice of animal or groups of animals involves different sets of available resource units. The definition of RSF requires that the resource which is being studied consists of discrete units. The statistical method often used to estimate the RSF is the logistic regression but DCM can also be used. The theory of DCM has been well developed for the analysis of data sets involving choices of products by humans, but it can also be applicable to the choice of habitat by animals, with some modifications. The comparison of the logistic regression with the DCM for one choice is made because the coefficient estimates of the logistic regression model include an intercept, which are not presented by the DCM. The objective of this work was to compare the estimates of the RSF obtained by applying the logistic regression and the DCM to the data set on habitat selection of the spotted owl (Strix occidentalis) in the north west of the United States. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000300011 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000300011 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-90162010000300011 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.67 n.3 2010 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936461945667584 |