scholarly journals 101 Evaluation of Growth Curve Functions for predicted weight in small holder crossbred dairy cattle in Tanzania

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 79-80
Author(s):  
Chinyere Ekine ◽  
Raphael Mrode ◽  
Edwin Oyieng ◽  
Daniel Komwihangilo ◽  
Gilbert Msuta ◽  
...  
Keyword(s):  
Growth Curve ◽  
Model Comparison ◽  
Predictive Ability ◽  
Nonlinear Least Squares ◽  
Information Criterion ◽  
Dairy Farms ◽  
Exponential Model ◽  
Males And Females ◽  
Small Holder ◽  
Two Parameter

Abstract Modelling the growth curve of animals provides information on growth characteristics and is important for optimizing management in different livestock systems. This study evaluated the growth curves of crossbred calves from birth to 30 months of age in small holder dairy farms in Tanzania using a two parameter (exponential), four different three parameters (Logistic, von Bertalanffy, Brody, Gompertz), and three polynomial functions. Predicted weights based on heart girth measurements of 623 male and 846 female calves born between 2016 and 2019 used in this study were from the African Dairy Genetic Gains (ADGG) project in selected milk sheds in Tanzania, namely Tanga, Kilimanjaro, Arusha, Iringa, Njomba and Mbeya. Each function was fitted separately to weight measurement of males and females adjusted for the effect of ward and season of birth using the nonlinear least squares (nls) functions in R statistical software. The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, all three polynomial and four parameter functions performed better and did not differ enough from each other in both males and females compared to the two-parameter exponential model. Predicted weight varied among the models and differed between males and females. The highest estimated weight was observed in the Brody model for both males (278.09 kg) and females (264.10 kg). Lowest estimated weight was observed in the exponential model. Estimated growth rate varied among models. For males, it ranged from 0.04 kg-0.08 kg and for females, from 0.05 kg-0.09 kg in the Brody model and logistic model respectively. Predictive ability across all fitted curves was low, ranging from 25% to approximately 29%. This could be due to the huge range of breed compositions in the evaluated crossbred calves which characterizes small holder dairy farms in this system and different levels of farm management.

scholarly journals Evaluation of Growth Curve Models for Body Weight in American Mink

Animals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 22
Author(s):  
Duy Ngoc Do ◽  
Younes Miar
Keyword(s):  
Body Weight ◽  
Growth Curve ◽  
Model Comparison ◽  
American Mink ◽  
Growth Curves ◽  
Information Criterion ◽  
Selection Response ◽  
Growth Curve Models ◽  
Body Weights ◽  
Males And Females

Modelling the growth curves of animals is important for optimizing the management and efficiency of animal production; however, little is known about the growth curves in American mink (Neovison vison). The study evaluated the performances of four three-parameter (Logistic, Gompertz, von Bertalanffy, and Brody), four four-parameter (Richards, Weibull, Bridges, and Janoscheck) and two polynomial models for describing the growth curves in mink. Body weights were collected from the third week of life to the week 31 in 738 black mink (373 males and 365 females). Models were fitted using the nls and nlsLM functions in stats and minpack.lm packages in R software, respectively. The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, Logistic and Richards were the best models for males and females, respectively. Four-parameter models had better performance compared to the other models except for Logistic model. The estimated maximum weight and mature growth rate varied among the models and differed between males and females. The results indicated that males and females had different growth curves as males grew faster and reached to the maximum body weight later compared to females. Further studies on genetic parameters and selection response for growth curve parameters are required for development of selection programs based on the shape of growth curves in mink.

scholarly journals Constant Information Design for Binary Response Data

1998 ◽  
Vol 3 ◽  
pp. 57
Author(s):  
K.A. Abdelbasit
Keyword(s):  
Binary Data ◽  
Information Matrix ◽  
Information Criterion ◽  
Exponential Model ◽  
Binary Response ◽  
Unknown Parameters ◽  
Response Data ◽  
Two Parameters ◽  
Parameter Case ◽  
Two Parameter

A major problem is designing experiments when the assumed model is nonlinear, is the dependence of the designs on the values of the unknown parameters we consider in this article designs for binary data and generalize the constant information criterion suggested by Fisher (1922). The criterion calls for designs that achieve a specific proportion of the total constant information. This leads to designs where dependence of Fisher’s information on the unknown parameters is very little, thus leading to constant variances. We show that such designs exist for any single parameter model, extending Fisher‘s result for the exponential model. We discuss the construction of such designs and investigate their performance as measured by the achievement of constant information. When two parameters are needed to specify the model , we show that experiments can be designed so that the determinant of the information matrix is independent of the parameters. Construction of designs and examining their performance are also investigated for the two parameter case.

scholarly journals Mathematical models to describe the growth curves of Vietnamese Ri chicken

2023 ◽  
Vol 83 ◽  
Author(s):  
T. H. Nguyen ◽  
C. X. Nguyen ◽  
M. Q. Luu ◽  
A. T. Nguyen ◽  
D. H. Bui ◽  
...  
Keyword(s):  
Growth Curve ◽  
Model Comparison ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Chicken Breed ◽  
Richards Model ◽  
Genetics And Genomics ◽  
Backyard Chicken

Abstract Ri chicken is the most popular backyard chicken breed in Vietnam, but little is known about the growth curve of this breed. This study compared the performances of models with three parameters (Gompertz, Brody, and Logistic) and models containing four parameters (Richards, Bridges, and Janoschek) for describing the growth of Ri chicken. The bodyweight of Ri chicken was recorded weekly from week 1 to week 19. Growth models were fitted using minpack.lm package in R software and Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and root mean square error (RMSE) were used for model comparison. Based on these criteria, the models having four parameters showed better performance than the ones with three parameters, and the Richards model was the best one for males and females. The lowest and highest value of asymmetric weights (α) were obtained by Bridges and Brody models for each of sexes, respectively. Age and weight estimated by the Richard model were 8.46 and 7.51 weeks and 696.88 and 487.58 g for males and for females, respectively. Differences in the growth curves were observed between males and female chicken. Overall, the results suggested using the Richards model for describing the growth curve of Ri chickens. Further studies on the genetics and genomics of the obtained growth parameters are required before using them for the genetic improvement of Ri chickens.

scholarly journals Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Reem Aljarallah ◽  
Samer A Kharroubi
Keyword(s):  
Monte Carlo ◽  
Down Syndrome ◽  
Mental Retardation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Univariate Analysis ◽  
Deviance Information Criterion ◽  
Predictive Ability ◽  
Information Criterion ◽  
Medical Genetic

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

scholarly journals Growth curve of Atlantoscia floridana (van Name) (Crustacea, Isopoda, Philosciidae) from a Brazilian Restinga Forest

2004 ◽  
Vol 21 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Paula Beatriz Araujo ◽  
Georgina Bond-Buckup
Keyword(s):  
Growth Rate ◽  
Growth Curve ◽  
Urban Areas ◽  
Field Data ◽  
Growth Curves ◽  
Rio Grande ◽  
Rio Grande Do Sul ◽  
Differential Growth ◽  
Estimate Life Expectancy ◽  
Males And Females

The terrestrial isopod Atlantoscia floridana (van Name, 1940) occurs from the U.S.A. (Florida) to Brazil and Argentina. In the southernmost Brazilian State, Rio Grande do Sul, the species is recorded in many localities, in urban and in non-urban areas. The growth curve of Atlantoscia floridana based on field data is presented. The specimens were sampled from April, 2000 to October, 2001 at the Reserva Biológica do Lami (RBL), Rio Grande do Sul. Captured individuals were sexed and had their cephalothorax width measured, with the data analyzed with von Bertalanffy's model. The growth curves for males and females are described, respectively, by the equations: Wt = 1.303 [1 - e-0.00941 (t + 50.37)] and Wt = 1.682 [1 - e-0.00575 (t + 59.13)]. The curves showed differential growth between sexes, where females reach a higher Wµ with a slower growth rate. Based on the growth curves it was also possible to estimate life expectancy for males and females.

scholarly journals Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

2021 ◽  
Author(s):  
Thomas C. van Leth ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Exponential Model ◽  
Rain Gauges ◽  
Wide Range ◽  
Spatio Temporal ◽  
Two Parameter

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

scholarly journals Constructing single-entry stem volume models for four economically important tree species of Greece

2021 ◽  
Vol 48 (2) ◽  
pp. 136-146
Author(s):  
Panagiotis P. Koulelis ◽  
Kostas Ioannidis
Keyword(s):  
Tree Species ◽  
National Level ◽  
Sustainable Use ◽  
Information Criterion ◽  
Cost Effective ◽  
Volume Estimation ◽  
Stem Volume ◽  
Nonlinear Regression Models ◽  
Cubic Polynomial ◽  
Two Parameter

Abstract Three different nonlinear regression models were tested for their ability to predict stem volume for economically important native tree species in Greece. Τhe models were evaluated using adjusted R square (Adj Rsqr) root mean square error (RMSE) and Akaike information criterion (AICc), where necessary. In general, the quadratic polynomial and cubic polynomial models and the two-parameter power models fit the data well. Although the two-parameter power function fit best for fir, oak, and beech trees, the cubic polynomial model produced the best fit statistics for black pine. Making forest inventory estimates often involves predicting tree volumes from only the diameter at breast height (DBH) and merchantable height. This study covers important gaps in fast and cost-effective methods for calculating the volume of tree species at national level. However, the increasing need for reliable estimates of inventory components and volume changes requires more accurate volume estimation techniques. Especially when those estimates concern the national inventory, those models must be validated using an entire range of age/diameter and site classes of each species before their extended use across the country to promote the sustainable use of forest resources.

scholarly journals Determinants of Smart Tourist Environmentally Responsible Behavior Using an Extended Norm-Activation Model

2020 ◽  
Vol 12 (12) ◽  
pp. 4934 ◽  
Author(s):  
Kiattipoom Kiatkawsin ◽  
Ian Sutherland ◽  
Seul Ki Lee
Keyword(s):  
Model Comparison ◽  
Predictive Ability ◽  
Total Sample ◽  
Tourism Industry ◽  
Activation Model ◽  
Research Attention ◽  
Environmentally Responsible Behavior ◽  
Responsible Behavior ◽  
Environmentally Responsible ◽  
Smart Technologies

The emergence of the smart tourism paradigm has shifted some research attention to the technologies that drive innovations. However, tourism destinations are not freed from the usual threats in the tourism industry. Environmental impacts have remained a fundamental concern for any destinations regardless of their adoption and incorporation of smart technologies. Tourists remain a critical source of harm inflicted on environmental systems. Thus, this present study aims to study smart tourists’ environmentally responsible behavior using an extended norm-activation model. The study model incorporates two new constructs measuring the involvement of culture and attitude towards cultural conservation as additional predictors of environmentally responsible behavior. A total sample of 554 is subjected to data analysis. The results support all proposed hypotheses. Both newly added constructs produce the largest total impact scores on the final construct. Model comparison between the study model and the original framework showed improved predictive ability while retaining superior fit.

scholarly journals B-39 Is the Latent Structure of Neurocognitive Domains Invariant Across Males and Females with Schizophrenia?

2019 ◽  
Vol 34 (6) ◽  
pp. 985-985
Author(s):  
M Becker ◽  
D Allen
Keyword(s):  
Cognitive Abilities ◽  
Mean Squared Error ◽  
Age Of Onset ◽  
Factor Model ◽  
Information Criterion ◽  
Latent Structure ◽  
Strong Basis ◽  
Best Fitting ◽  
Competing Models ◽  
Males And Females

Abstract Objective Differences between males and females with schizophrenia have been identified for numerous important disorder-related variables including age of onset, severity, and course, among others. Evidence suggests there also may be differences in intellectual functioning and possibly specific cognitive deficits. This study examined differences in the latent structure of cognitive abilities between males and females with schizophrenia. Method Participants included 659 males (age X̄ = 38.25, 64.5% Caucasian, education X̄ = 11.69) and 209 females (age X̄ = 40.52, 55.5% Caucasian, education X̄ = 11.72) with schizophrenia who were evaluated with neuropsychological tests as part of a large multicenter randomized control trial of antipsychotic medications (CATIE). Confirmatory factor analysis (CFA) was used to test four competing models based on prior CFA of the CATIE data. Model accuracy was evaluated using Comparative Fit Index (CFI), Root Mean Squared Error of Approximation (RMSEA), and Akaike’s Information Criterion (AIC). Results CFAs were completed for each sex using EQS 6.3. Models included one-factor, five-factor, six-factor, and a hierarchical model in which five factors load on a single factor “g”. The six-factor model was the best fitting for both males and females. Conclusions The results indicated that a six-factor model of neurocognition is the best fitting model for both males and females. The results also suggest that the latent structure of cognitive abilities is similar for both sexes. This provides a strong basis for uniform interpretation of neuropsychological domains across males and females, although there may be sex-related differences in patterns and severity of deficit in each domain.

scholarly journals Model comparison and estimation of genetic parameters for body weight in commercial broilers

2013 ◽  
Vol 93 (1) ◽  
pp. 67-77 ◽  
Author(s):  
G. Maniatis ◽  
N. Demiris ◽  
A. Kranis ◽  
G. Banos ◽  
A. Kominakis
Keyword(s):  
Body Weight ◽  
Akaike Information Criterion ◽  
Model Evaluation ◽  
Model Comparison ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
Biological Properties ◽  
Genetic Covariance ◽  
Estimation Of Genetic Parameters

Maniatis, G., Demiris, N., Kranis, A., Banos, G. and Kominakis, A. 2013. Model comparison and estimation of genetic parameters for body weight in commercial broilers. Can. J. Anim. Sci. 93: 67–77. The availability of powerful computing and advances in algorithmic efficiency allow for the consideration of increasingly complex models. Consequently, the development and application of appropriate statistical procedures for model evaluation is becoming increasingly important. This paper is concerned with the application of an alternative model determination criterion (conditional Akaike Information Criterion, cAIC) in a large dataset comprising 203 323 body weights of broilers, pertaining to 7 (BW7) and 35 (BW35) days of age. Seven univariate and seven bivariate models were applied. Direct genetic, maternal genetic and maternal environmental (c2) effects were estimated via REML. The model evaluation criteria included conditional Akaike Information Criterion (cAIC), Bayesian Information Criterion (BIC) and the standard Akaike Information Criterion (henceforth marginal; mAIC). According to cAIC the best-fitting model included direct genetic, maternal genetic and c2 effects. Maternal heritabilities were low (0.10 and 0.03) compared to the direct heritabilities (0.17 and 0.21), while c2 was 0.05 and 0.04 for BW7 and BW35, respectively. BIC and mAIC favoured a model that additionally included a direct-maternal genetic covariance, resulting in highly negative direct-maternal genetic correlations (−0.47 and −0.64 for BW7 and BW35, respectively) and higher direct heritabilities (0.25 and 0.28 for BW7 and BW35, respectively). Results suggest that cAIC can select different animal models than mAIC and BIC with different biological properties.

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