Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses
Many biological quantities cannot be measured directly but rather need to be estimated from models. Estimates from models are statistical objects with variance and, when derived simultaneously, covariance. It is well known that their variance–covariance (VC) matrix must be considered in subsequent analyses. Although it is always preferable to carry out the proposed analyses on the raw data themselves, a two‐step approach cannot always be avoided. This situation arises when the parameters of a multinomial must be regressed against a covariate. The Delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. Implementing the Delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. This paper proposes a how‐to guide for calculating the correct VC matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. For illustrative purpose, we focus on variables calculated in capture–recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances.
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Subjects: | U10 - Informatique, mathématiques et statistiques, P01 - Conservation de la nature et ressources foncières, L60 - Taxonomie et géographie animales, analyse de données, modélisation, analyse de covariance, écologie animale, dynamique des populations, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_230ab86c, http://aims.fao.org/aos/agrovoc/c_28833, http://aims.fao.org/aos/agrovoc/c_427, http://aims.fao.org/aos/agrovoc/c_6111, |
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dig-cirad-fr-5910462024-12-18T13:39:09Z http://agritrop.cirad.fr/591046/ http://agritrop.cirad.fr/591046/ Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses. Guery Loreleï, Rouan Lauriane, Descamps Sébastien, Bêty Joël, Fernández-Chacón Albert, Gilchrist Grant, Pradel Roger. 2019. Ecology and Evolution, 9 (2) : 818-824.https://doi.org/10.1002/ece3.4827 <https://doi.org/10.1002/ece3.4827> Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses Guery, Loreleï Rouan, Lauriane Descamps, Sébastien Bêty, Joël Fernández-Chacón, Albert Gilchrist, Grant Pradel, Roger eng 2019 Wiley Ecology and Evolution U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières L60 - Taxonomie et géographie animales analyse de données modélisation analyse de covariance écologie animale dynamique des populations http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_28833 http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_6111 Many biological quantities cannot be measured directly but rather need to be estimated from models. Estimates from models are statistical objects with variance and, when derived simultaneously, covariance. It is well known that their variance–covariance (VC) matrix must be considered in subsequent analyses. Although it is always preferable to carry out the proposed analyses on the raw data themselves, a two‐step approach cannot always be avoided. This situation arises when the parameters of a multinomial must be regressed against a covariate. The Delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. Implementing the Delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. This paper proposes a how‐to guide for calculating the correct VC matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. For illustrative purpose, we focus on variables calculated in capture–recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/591046/1/Guery_et_al-2019-Ecology_and_Evolution.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1002/ece3.4827 10.1002/ece3.4827 info:eu-repo/semantics/altIdentifier/doi/10.1002/ece3.4827 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1002/ece3.4827 |
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U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières L60 - Taxonomie et géographie animales analyse de données modélisation analyse de covariance écologie animale dynamique des populations http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_28833 http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_6111 U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières L60 - Taxonomie et géographie animales analyse de données modélisation analyse de covariance écologie animale dynamique des populations http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_28833 http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_6111 |
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U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières L60 - Taxonomie et géographie animales analyse de données modélisation analyse de covariance écologie animale dynamique des populations http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_28833 http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_6111 U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières L60 - Taxonomie et géographie animales analyse de données modélisation analyse de covariance écologie animale dynamique des populations http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_28833 http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_6111 Guery, Loreleï Rouan, Lauriane Descamps, Sébastien Bêty, Joël Fernández-Chacón, Albert Gilchrist, Grant Pradel, Roger Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses |
description |
Many biological quantities cannot be measured directly but rather need to be estimated from models. Estimates from models are statistical objects with variance and, when derived simultaneously, covariance. It is well known that their variance–covariance (VC) matrix must be considered in subsequent analyses. Although it is always preferable to carry out the proposed analyses on the raw data themselves, a two‐step approach cannot always be avoided. This situation arises when the parameters of a multinomial must be regressed against a covariate. The Delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. Implementing the Delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. This paper proposes a how‐to guide for calculating the correct VC matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. For illustrative purpose, we focus on variables calculated in capture–recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières L60 - Taxonomie et géographie animales analyse de données modélisation analyse de covariance écologie animale dynamique des populations http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_28833 http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_6111 |
author |
Guery, Loreleï Rouan, Lauriane Descamps, Sébastien Bêty, Joël Fernández-Chacón, Albert Gilchrist, Grant Pradel, Roger |
author_facet |
Guery, Loreleï Rouan, Lauriane Descamps, Sébastien Bêty, Joël Fernández-Chacón, Albert Gilchrist, Grant Pradel, Roger |
author_sort |
Guery, Loreleï |
title |
Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses |
title_short |
Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses |
title_full |
Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses |
title_fullStr |
Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses |
title_full_unstemmed |
Covariate and multinomial: Accounting for distance in movement in capture-recapture analyses |
title_sort |
covariate and multinomial: accounting for distance in movement in capture-recapture analyses |
publisher |
Wiley |
url |
http://agritrop.cirad.fr/591046/ http://agritrop.cirad.fr/591046/1/Guery_et_al-2019-Ecology_and_Evolution.pdf |
work_keys_str_mv |
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_version_ |
1819043857075011584 |