Principaux modèles utilisés en régression logistique/Main models used in logistic regression

Regression is a commonly used technique for decribing the relationship between a response variable and one or more explanatory variables. When the response variable is a categorical variable, usual regression based on ordinary least squares should be replaced by logistic regression. Binary logistic...

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Published inBiotechnologie, agronomie, société et environnement Vol. 15; no. 3; p. 425
Main Authors Gillet, Adeline, Brostaux, Yves, Palm, Rodolphe
Format Journal Article
LanguageFrench
Published Gembloux Les Presses Agronomiques de Gembloux 01.07.2011
Subjects
Regression analysis
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Summary:Regression is a commonly used technique for decribing the relationship between a response variable and one or more explanatory variables. When the response variable is a categorical variable, usual regression based on ordinary least squares should be replaced by logistic regression. Binary logistic regression should be used to perform a regression on a dichotomous response. Nominal polytomous logistic regression applies to a categorical response variable that has more than two levels with no natural ordering. And ordinal polytomous logistic regression is used when the response is a categorical variable that has more than two levels with a natural ordering. This note gives an overview of these logistic regression methods and describes three models commonly used when performing ordinal logistic regression. These models are illustrated by an example related to oak decline in the Walloon Region (Belgium). [PUBLICATION ABSTRACT]
ISSN:1370-6233
1780-4507