Abstract
This paper discusses the Bayesian approach as an alternative to the classical analysis of nonlinear models for growth curve data in Japanese quail. A Bayesian nonlinear modeling method is introduced and compared with the classical nonlinear least squares (NLS) method using three non-linear models that are widely used in modeling the growth data of poultry. The Gompertz, Richards and Logistic models were fitted to 499 Japanese quail weekly averaged body weight data. Normal prior was assumed for all growth curve parameters of the models with assuming Jeffreys' non-informative prior for residual variances. Models were compared based on the Bayesian measure of fit, deviance information criterion (DIC), and our results indicated the better fit of Gompertz and Richards models than the Logistic model to our data. Moreover, the parameter estimates of the models fitted by both approaches showed only small differences.
Highlights
While a large number of linear functions are adequately used to model a wide variety of relationships between variables, many biological phenomena require non-linear functions, in which the response varies as a function of time
Their approximate standard errors and 95% confidence intervals obtained from the classical approach (NLS) for the three growth functions are given in Table 1 for completeness
Gompertz function has the best fit to the data in terms of model selection criteria, Akaike’s information criterion (AIC) and Bayesian information criterion (BIC), followed by Richards and Logistic functions using classical approach, nonlinear least squares (NLS)
Summary
While a large number of linear functions are adequately used to model a wide variety of relationships between variables, many biological phenomena require non-linear functions, in which the response varies as a function of time. Time-related changes of a phenomenon are of particular importance in a wide range of disciplines such as biology, agriculture, economics, medicine, crop science, etc. Growth curves illustrating these changes allow the data to be summarized by a few number of parameters known as growth curve parameters. There has been a great deal of interest in modeling the growth of poultry. Several other studies have modeled the growth of other species of poultry (Tzeng & Becker, 1981; Emmans, 1995;Akbas & Oguz, 1998; Aggrey, 2002;Ahmadi & Golian, 2008)
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