ESTIMATION OF GROWTH PARAMETERS USING NON-LINEAR MIXED MODEL AND COMPARISON WITH FIXED EFFECT MODEL IN ANIMALS

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Date
2015
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INDIAN AGRICULTURAL STATISTICS RESEARCH INSTITUTE INDIAN AGRICULTURAL RESEARCH INSTITUTE NEW DELHI
Abstract
Modeling growth of an animal is a complex process, because it requires describing longitudinal measurements with few parameters with biological interpretation. With longitudinal data, the variance of observations may increase with time (age), and repeated measurements of an individual over time are correlated. The non-independence of data violates a key assumption underlying many statistical procedures and has been ignored in most traditional non-linear fixed effect models. A solution to this problem is the use of non-linear mixed effect models (NLMM). Mixed effects models for longitudinal data such as growth have become of interest because of their flexible covariance structure and their ability to handle unbalanced data. A NLMM makes it possible to account for random covariates before testing for fixed effects and control autocorrelation in repeated measures. Nonlinear mixed effect models also account for multiple sources of heterogeneity in data through the inclusion of random effects in the models. In this study, growth data of Goat and Pig has been used. Attempt has been made to develop the Von-bertalanffy mixed model. Logistic, Gompertz and Von-bertalanffy fixed and mixed models have been explored for these data. Comparison of the models i.e. between fixed and mixed type of the same model and among different fixed and mixed models has been attempted. The goodness of fit statistics like i.e. Mean Square Error (MSE) and Root Mean Square Error (RMSE) of the fitted models has been computed. The parameters of the best fitted models along with their corresponding standard error are estimated. It has been found that Logistic mixed effect model is performed best for the Goat and Pig data. The prediction of this model is also better. Computer programs in SAS and R have been developed for the data analysis. In literature it has been found that Logistic mixed model is most appropriate for animals. So it may be concluded that the logistic mixed effect model can be used for other animal.
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T-9251
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