Periodic Logistic Regression

We show how to use logistic regression in situations where the phenomenon under consideration is periodic, i.e., follows a cyclic pattern. The method is illustrated by using data for the snail Potamopyrgus antipodarum, which exhibits a 24-hr periodic pattern of foraging. Snails are found on the top...

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Bibliographic Details
Published in:Ecology (Durham) 1999-10, Vol.80 (7), p.2254-2260
Main Authors: Flury, Bernard D., Levri, Edward P.
Format: Article
Language:English
Subjects:
Animal behavior
Animal, plant and microbial ecology
Biological and medical sciences
Biotic communities
Circadian rhythm
Cosine function
cyclic phenomena
Ecological modeling
Ecology
Female animals
Foraging
Freshwater
Fundamental and applied biological sciences. Psychology
Gastropoda
General aspects. Techniques
Logistic regression
Maximum likelihood estimation
Methods and techniques (sampling, tagging, trapping, modelling...)
Mollusks
Parametric models
Potamopyrgus antipodarum
Regression analysis
Sine function
Snails
statistical modeling
Theoretical and Statistical Ecology
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Summary:We show how to use logistic regression in situations where the phenomenon under consideration is periodic, i.e., follows a cyclic pattern. The method is illustrated by using data for the snail Potamopyrgus antipodarum, which exhibits a 24-hr periodic pattern of foraging. Snails are found on the top or bottom of rocks, depending on time of day. We show how periodic logistic regression models can be fitted simultaneously to several groups of data, taking into account that the groups may exhibit similar (but not identical) cyclic patterns. The proposed procedure is applicable to ecological studies that analyze the variability in a dichotomous response variable exhibiting a cyclic pattern over time.
ISSN:0012-9658
1939-9170
DOI:10.1890/0012-9658(1999)080[2254:PLR]2.0.CO;2