Estimation of non-linear growth models by linearization : a simulation study using a Gompertz function

A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be...

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Bibliographic Details
Published in:Genetics selection evolution (Paris) 2006-06, Vol.38 (4), p.343-358, Article 343
Main Authors: VUORI, Kaarina, STRANDEN, Ismo, SEVON-AIMONEN, Marja-Liisa, MÄNTYSAARI, Esa A
Format: Article
Language:English
Subjects:
Algorithms
Animal breeding
Animal genetics
Animal productions
Animals
Biological and medical sciences
Breeding
breeding values
Classical genetics, quantitative genetics, hybrids
Fundamental and applied biological sciences. Psychology
Genetics
Genetics of eukaryotes. Biological and molecular evolution
Gompertz function
Life Sciences
likelihood approximation
Mathematical models
Methods
Models, Biological
non-linear mixed effects
Nonlinear Dynamics
Swine - growth & development
Terrestrial animal productions
variance components
Vertebrata
Vertebrates
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Summary:A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.
ISSN:0999-193X
1297-9686
1297-9686
DOI:10.1186/1297-9686-38-4-343