scholarly journals Mixed-effects growth curves in the valuation of Nellore sires

2009 ◽  
Vol 66 (1) ◽  
pp. 84-89
Author(s):  
Suely Ruiz Giolo ◽  
Robin Henderson ◽  
Clarice Garcia Borges Demétrio
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Production Systems ◽  
Growth Curves ◽  
Logistic Growth ◽  
Cattle Breeding ◽  
Growth Curve Models ◽  
Fixed And Random Effects ◽  
Curve Model ◽  
Breeding Programmes

Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.

Analysis of the Development Trend of Chinese Textile Industry Based on Growth Curve Model

2011 ◽  
Vol 332-334 ◽  
pp. 1386-1389 ◽  
Author(s):  
Tao Ma ◽  
Hong Zhao
Keyword(s):  
Growth Curve ◽  
Textile Industry ◽  
Development Trend ◽  
Annual Growth Rate ◽  
Growth Curve Model ◽  
Growth Curve Models ◽  
Formative Stage ◽  
Curve Model ◽  
Important Position ◽  
The Development Trend

Growth-curve models are generalized multivariate analysis-of-variance models. This kind of method was widely used in the prediction of industry development cycle. The textile industry occupies an important position in the national economy of China. The paper analyzed the development trend of Chinese textile industry based on growth curve model and found out that Chinese textile industry is in the formative stage and is about to begin entered into matured period. The next five years the average annual growth rate of Chinese textile industry can reach more than 9 percent, and the textile industry output will reach 6 trillions in 2015.

scholarly journals Comparison of growth curves of two genotypes of dairy goats using nonlinear mixed models

2013 ◽  
Vol 152 (5) ◽  
pp. 829-842 ◽  
Author(s):  
J. G. L. REGADAS FILHO ◽  
L. O. TEDESCHI ◽  
M. T. RODRIGUES ◽  
L. F. BRITO ◽  
T. S. OLIVEIRA
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Mixed Model ◽  
Growth Curves ◽  
Growth Patterns ◽  
Dairy Goat ◽  
Residual Variance ◽  
Nonlinear Mixed Model ◽  
Error Structure ◽  
Mixed Model Methodology

SUMMARYThe objective of the current study was to assess the use of nonlinear mixed model methodology to fit the growth curves (weightv.time) of two dairy goat genotypes (Alpine, +A and Saanen, +S). The nonlinear functions evaluated included Brody, Von Bertalanffy, Richards, Logistic and Gompertz. The growth curve adjustment was performed using two steps. First, random effectsu1,u2andu3were linked to the asymptotic body weight (β1), constant of integration (β2) and rate constant of growth (β3) parameters, respectively. In addition to a traditional fixed-effects model, four combinations of models were evaluated using random variables: all parameters associated with random effects (u1,u2andu3), onlyβ1andβ2(u1andu2), onlyβ1andβ3(u1andu3) and onlyβ1(u1). Second, the fit of the best adjusted model was refined by using the power variance and modelling the error structure. Residual variance ($\sigma _e^2 $) and the Akaike information criterion were used to evaluate the models. After the best fitting model was chosen, the genotype curve parameters were compared. The residual variance was reduced in all scenarios for which random effects were considered. The Richards (u1andu3) function had the best fit to the data. This model was reparameterized using two isotropic error structures for unequally spaced data, and the structure known in the literature as SP(MATERN) proved to be a better fit. The growth curve parameters differed between the two genotypes, with the exception of the constant that determines the proportion of the final size at which the inflection point occurs (β4). The nonlinear mixed model methodology is an efficient tool for evaluating growth curve features, and it is advisable to assign biologically significant parameters with random effects. Moreover, evaluating error structure modelling is recommended to account for possible correlated errors that may be present even when using random effects. Different Richard growth curve parameters should be used for the predominantly Alpine and Saanen genotypes because there are differences in their growth patterns.

Continuity and cascade in offspring of bipolar parents: A longitudinal study of externalizing, internalizing, and thought problems

2010 ◽  
Vol 22 (4) ◽  
pp. 849-866 ◽  
Author(s):  
Bonnie Klimes-Dougan ◽  
Jeffrey D. Long ◽  
Chih-Yuan Steven Lee ◽  
Donna S. Ronsaville ◽  
Philip W. Gold ◽  
...  
Keyword(s):  
At Risk ◽  
Growth Curve ◽  
Internalizing Problems ◽  
Growth Curves ◽  
Entire Group ◽  
Childhood And Adolescence ◽  
Medical Problems ◽  
Growth Curve Models ◽  
Thought Problems ◽  
Novel Approach

AbstractThere is growing evidence that many offspring of bipolar parents will develop moderate to severe forms of psychopathology during childhood and adolescence. The purpose of this study was to apply growth curve models to evaluate developmental progression with regard to continuity and cascades representative within the context of a family risk study of bipolar disorder (BD). Repeated assessments of externalizing, internalizing, and thought problems, spanning more than a decade, were examined in a total of 94 offspring of parents with BD (O-BD), major depressive disorder (O-UNI), or no significant psychiatric or medical problems (O-WELL). Continuity was defined by the growth curve of the O-WELL group who exhibited low levels of problems from early childhood through late adolescence. Discontinuity, as evidenced by greater complexity of growth curves relative to the O-WELL group, was exhibited in the at- risk offspring groups for internalizing problems. Different patterns of developmental cascades were supported for the at-risk group with O-UNI showing a robust cascade from self-regulatory deficits (externalizing problems) to internalizing problems. There was also support for a cascade from self-regulatory deficits to thought problems across the entire group (with some support that this pattern was accounted for primarily by O-BD). This study not only serves to advance our understanding of the risks associated with a family history of BD, but also provides a novel approach to examining developmental cascades.

Relationship Between Two Stock–Recruitment Curves

1977 ◽  
Vol 34 (3) ◽  
pp. 425-428 ◽  
Author(s):  
L. L. Eberhardt
Keyword(s):  
Difference Equation ◽  
Population Growth ◽  
Population Size ◽  
Growth Curve ◽  
Population Biology ◽  
Population Regulation ◽  
Growth Curves ◽  
Equation Model ◽  
Logistic Growth ◽  
Stock Recruitment

The Beverton and Holt and Ricker stock–recruitment curves can be used to generate population growth curves. The Beverton and Holt curve is then identical to a difference equation model for the logistic growth curve, and may be derived in terms of equations for linearly density-dependent population regulation. The same equations lead to the Ricker curve if the density-regulating effect is assumed to depend only on population size at the beginning of the interval between generations. At low rates of population growth, the Ricker curve approaches that of Beverton and Holt. The two curves appear to represent certain concepts known in population biology as "r and K selection."

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