scholarly journals Modelling lactation curves for fat-to-protein ratio of milk in the first three lactations of Polish Holstein-Friesian cows

2021 ◽  
Vol 51 (3) ◽  
pp. 310-321
Author(s):  
A. Satola
Keyword(s):  
Correlation Coefficient ◽  
Fixed Effects ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Random Effect ◽  
Information Criterion ◽  
Energy Status ◽  
Mathematical Functions ◽  
Milk Traits ◽  
Protein Ratio

Among milk traits, fat-to-protein ratio (FPR) is considered a potential measure of a cow’s energy status and is one of the selection criteria necessary to improve metabolic stability. Further genetic analyses require an appropriate model that describes the pattern of FPR changes throughout lactation. The objective of the study was to examine five mathematical functions to describe the lactation curve for FPR in the first three lactations of Polish Holstein-Friesians. The dataset contained FPR records for 5690 cows in the first lactation, 4081 cows in the second, and 2636 cows in the third lactation based on 48908, 34706, and 22097 test-day (TD) records, respectively. Using the MIXED procedure of SAS statistical analytics software, ten linear models (five with fixed effects only, and five with the additional random effect of cow) were fitted to the TD records. The goodness of fit was tested with Akaike's information criterion, residual variances and the correlation coefficient between the actual and estimated values. The model proposed by Ali and Schaeffer (1987) had the best fit to FPR in the first three parities, and the model of Wilmink (1987) provided the worst fit. The correlation coefficient between the actual and the estimated values of FPR was higher for models that included the random cow effect compared with models without this effect.

scholarly journals Including random effects in statistical models in ecology: fewer than five levels?

2021 ◽  
Author(s):  
Dylan G.E. Gomes
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Linear Models ◽  
Random Effect ◽  
Mixed Effects ◽  
Parameter Estimates ◽  
Type I ◽  
Type I Errors ◽  
Ecological Sites ◽  
Generalized Linear Mixed Effects

AbstractAs generalized linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common guideline is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random effects terms (such as ecological sites, individuals, or populations) difficult, but it need not muddy one’s ability to estimate fixed effects terms – which are often of primary interest in ecology. Here, I simulate ecological datasets and fit simple models and show that having too few random effects terms does not influence the parameter estimates or uncertainty around those estimates for fixed effects terms. Thus, it should be acceptable to use fewer levels of random effects if one is not interested in making inference about the random effects terms (i.e. they are ‘nuisance’ parameters used to group non-independent data). I also use simulations to assess the potential for pseudoreplication in (generalized) linear models (LMs), when random effects are explicitly ignored and find that LMs do not show increased type-I errors compared to their mixed-effects model counterparts. Instead, LM uncertainty (and p values) appears to be more conservative in an analysis with a real ecological dataset presented here. These results challenge the view that it is never appropriate to model random effects terms with fewer than five levels – specifically when inference is not being made for the random effects, but suggest that in simple cases LMs might be robust to ignored random effects terms. Given the widespread accessibility of GLMMs in ecology and evolution, future simulation studies and further assessments of these statistical methods are necessary to understand the consequences of both violating and blindly following simple guidelines.

scholarly journals Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 560
Author(s):  
Elena Lázaro ◽  
David Makowski ◽  
Joaquín Martínez-Minaya ◽  
Antonio Vicent
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Random Effects ◽  
Fixed Effects ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Support Systems ◽  
Disease Incidence ◽  
Meta Analysis ◽  
Field Trials

Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the results of 67 experiments assessing DSSs. Disease incidence data were obtained from published peer-reviewed field trials comparing untreated controls, calendar-based and DSS-based fungicide programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing incidence. All models were fit using both frequentist and Bayesian estimation procedures and the results compared. Model including random effects showed better performance in terms of AIC or DIC and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results. Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based and DSS-based programs provided similar reductions in disease incidence, further supporting the efficacy of DSSs.

scholarly journals Why analyze germination experiments using Generalized Linear Models?

2018 ◽  
Vol 40 (3) ◽  
pp. 281-287 ◽  
Author(s):  
Fábio Janoni Carvalho ◽  
Denise Garcia de Santana ◽  
Lúcio Borges de Araújo
Keyword(s):  
Generalized Linear Models ◽  
Binomial Distribution ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Binomial Model ◽  
Inappropriate Use ◽  
Copaiba Oil ◽  
Germination Experiments ◽  
Combined Data

Abstract: We compared the goodness of fit and efficiency of models for germination. Generalized Linear Models (GLMs) were performed with a randomized component corresponding to the percentage of germination for a normal distribution or to the number of germinated seeds for a binomial distribution. Lower levels of Akaikes’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) combined, data adherence to simulated envelopes of normal plots and corrected confidence intervals for the means guaranteed the binomial model a better fit, justifying the importance of GLMs with binomial distribution. Some authors criticize the inappropriate use of analysis of variance (ANOVA) for discrete data such as copaiba oil, but we noted that all model assumptions were met, even though the species had dormant seeds with irregular germination.

Modelling lactation curve for milk fat to protein ratio in Iranian buffaloes (Bubalus bubalis) using non-linear mixed models

2016 ◽  
Vol 83 (3) ◽  
pp. 334-340 ◽  
Author(s):  
Navid Ghavi Hossein-Zadeh
Keyword(s):  
Mixed Models ◽  
Milk Fat ◽  
Goodness Of Fit ◽  
Linear Mixed Models ◽  
Information Criterion ◽  
Protein Ratio ◽  
The Third ◽  
Lactation Curve ◽  
Non Linear ◽  
Best Fit

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (−2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

scholarly journals Evaluation of non-linear models to describe development and fertility of codling moth at constant temperatures

2017 ◽  
Vol 20 (1) ◽  
pp. 3 ◽  
Author(s):  
H. Ranjbar Aghdam ◽  
Y. Fathipour ◽  
D. C. Kontodimas
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Developmental Rate ◽  
Codling Moth ◽  
Coefficient Of Determination ◽  
Temperature Threshold ◽  
Optimum Temperature ◽  
Non Linear ◽  
Lower Temperature

Developmental rate of immature stages and age-specific fertility of females of codling moth at constant temperatures was modeled using non-linear models. The equations of Enkegaard, Analytis, and Bieri 1 and 2 were evaluated based on the value of adjusted R2 (R2adj) and Akaike information criterion (AIC) besides coefficient of determination (R2) and residual sum of squares (RSS). All models have goodness of fit to data especially for development [R2, R2adj, RSS and AIC ranged 0.9673-0.9917, 0.8601-0.9861, 0.08-6.7x10-4 and (-75.29) – (-46.26) respectively]. Optimum temperature (Topt) and upper threshold (Tmax) were calculated accurately (Topt and Tmax ranged 29.9-31.2oC and 35.9-36.7oC) by all models. Lower temperature threshold (Tmin) was calculated accurately by Bieri-1 model (9,9-10,8oC) whereas Analytis model (7,0-8,4oC) underestimated it. As far as fertility is concerned the respective values were better fitted near the optimum temperature (in 30oC) [R2 ,R2adj, RSS and AIC ranged 0,6966-0,7744, 0,5756-0,6455, 2,44-3,33 x10-4 and (-9,15)-7,15 respectively].

scholarly journals Comparison of the parameters of the lactation curve between normal and difficult calvings in Iranian Holstein cows

2019 ◽  
Vol 17 (1) ◽  
pp. e0401
Author(s):  
Navid Ghavi Hossein-Zadeh
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Lactation Curve ◽  
Non Linear ◽  
Best Fit

To evaluate effect of dystocia on the lactation curve characteristics for milk yield and composition in Holstein cows, six non-linear models (Brody, Wood, Sikka, Nelder, Dijkstra and Rook) were fitted on 5,917,677 test day records for milk yield (MY), fat (FP) and protein (PP) percentages, fat to protein ratio (FPR) and somatic cell score (SCS) of 643,625 first lactation Holstein cows with normal calving or dystocia from 3146 herds which were collected by the Animal Breeding Center of Iran. The models were tested for goodness of fit using adjusted coefficient of determination, root means square error, Akaike’s information criterion and Bayesian information criterion. Rook model provided the best fit of the lactation curve for MY and SCS in normal and difficult calvers and dairy cows with dystocia for FP. Dijkstra model provided the best fit of the lactation curve for PP and FPR in normal and difficult calvers and dairy cows with normal calving for FP. Dairy cows with dystocia had generally lower 100-d, 200-d and 305-d cumulative milk yield compared with normal calvers. Time to the peak milk yield was observed later for difficult calvers (89 days in milk vs. 79 days in milk) with lower peak milk yield (31.45 kg vs. 31.88 kg) compared with normal calvers. Evaluation of the different non-linear models indicated that dystocia had important negative effects on milk yield and lactation curve characteristics in dairy cows and it should be reduced as much as possible in dairy herds.

scholarly journals Choice of non-linear models to determine the growth curve of meat-type quail

2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Marta Jeidjane Borges Ribeiro ◽  
Fabyano Fonseca Silva ◽  
Maíse dos Santos Macário ◽  
José Aparecido Santos de Jesus ◽  
Claudson Oliveira Brito ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Metabolizable Energy ◽  
Coefficient Of Determination ◽  
Parameter Estimates ◽  
Coturnix Coturnix ◽  
Non Linear ◽  
Meat Type

ABSTRACT: The objective of this study was to compare non-linear models fitted to the growth curves of quail to determine which model best describes their growth and check the similarity between models by analyzing parameter estimates.Weight and age data of meat-type European quail (Coturnix coturnix coturnix) of three lines were used, from an experiment in a 2 × 4 factorial arrangement in a completely randomized design, consisting of two metabolizable energy levels, four crude protein levels and six replicates. The non-linear Brody, Von Bertalanffy, Richards, Logistic and Gompertz models were used. To choose the best model, the Adjusted Coefficient of Determination, Convergence Rate, Residual Mean Square, Durbin-Watson Test, Akaike Information Criterion and Bayesian Information Criterion were applied as goodness-of-fit indicators. Cluster analysis was performed to check the similarity between models based on the mean parameter estimates. Among the studied models, Richards’ was the most suitable to describe the growth curves. The Logistic and Richards models were considered similar in the analysis with no distinction of lines as well as in the analyses of Lines 1, 2 and 3.

scholarly journals Parametric Frailty Model with Time-Dependent Covariates

2020 ◽  
Vol 8 (5) ◽  
pp. 952-956
Keyword(s):  
Goodness Of Fit ◽  
Random Effect ◽  
Real Life ◽  
Frailty Model ◽  
Information Criterion ◽  
Monte Carlo Integration ◽  
Time Dependent ◽  
Estimation Of Parameters ◽  
Time Dependent Covariates ◽  
Parametric Frailty

The parametric frailty model has been used in this study where, the term frailty is used to represent an unobservable random effect shared by subjects with similar (unmeasured) risks in the analysis of mortality rate. In real-life environment, the application of frailty models have been widely used by biostatistician, economists and epidemiologist to donate proneness to disease, accidents and other events because there are persistent differences in susceptibility among individuals. When heterogeneity is ignored in a study of survival analysis the result will produce an incorrect estimation of parameters and standard errors. This study used gamma and Weibull distribution for the frailty model. The first objective of this study is to investigate parametric model with time dependent covariates on frailty model. The derivation is using either classical maximum likelihood or Monte Carlo integration. The second objective is to measure the effectiveness of Gamma and Weibull frailty model with and without time-dependent covariates. This is done by calculating the root mean square error (RMSE). The last objective is to assess the goodness of fit of Gamma and Weibull frailty model with and without time-dependent covariates using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Simulation is used in order to obtain the RMSE, AIC ad BIC value if time-dependent covariate does not exists. Between both models with time-dependent covariate, Weibull frailty distribution has lower AIC and BIC compared to Gamma frailty distribution. Therefore, Weibull frailty distribution with time-dependent covariate is preferable when a time-dependent covariate exists in a data.

scholarly journals Analysis of Fluid Velocity inside an Agricultural Sprayer Using Generalized Linear Mixed Models

2020 ◽  
Vol 10 (15) ◽  
pp. 5029
Author(s):  
Ángel Javier Aguirre ◽  
Guillermo E. Guevara-Viera ◽  
Carlos S. Torres-Inga ◽  
Raúl V. Guevara-Viera ◽  
Antonio Boné ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Goodness Of Fit ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Fluid Velocity ◽  
Random Effect ◽  
Information Criteria ◽  
Liquid Level ◽  
Residual Variance ◽  
Operational Parameters

The fluid velocity inside the tank of agricultural sprayers is an indicator of the quality of the mixture. This study aims to formulate the best generalized linear mixed model to infer the fluid velocity inside a tank under specific operational parameters of the agitation system, such as liquid level, circuit pressures, and number of active nozzles. A complex model was developed that included operational parameters as fixed effects (FE) and the section of the tank as the random effect. The goodness of fit of the model was evaluated by considering the lowest values of Akaike’s information criteria and Bayesian information criterion, and by estimating the residual variance. The gamma distribution and log-link function enhanced the goodness of fit of the best model. The Toeplitz structure was chosen as the structure of the covariance matrix. SPSS and SAS software were used to compute the model. The analysis showed that the greatest influence on the fluid velocity was exerted by the liquid level in the tank, followed by the circuit pressure and, finally, the number of active nozzles. The development presented here could serve as a guide for formulating models to evaluate the efficiency of the agitation system of agricultural sprayers.

scholarly journals PSX-19 Factors affecting growth and carcass trait performance of Canadian heavy lambs

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 457-457
Author(s):  
Erin Massender ◽  
Luiz Brito ◽  
Angela Cánovas ◽  
Delma Kennedy ◽  
Flavio Schenkel
Keyword(s):  
Evaluation System ◽  
Fixed Effects ◽  
Linear Models ◽  
Random Effect ◽  
Growth Trait ◽  
Carcass Trait ◽  
Classification Systems ◽  
Carcass Weight ◽  
Mixed Linear Models ◽  
Factors Affecting

Abstract The profitability of meat lamb production is strongly dependent on growth and carcass trait performance of market lambs. The objective of this research was to test the significance of non-genetic factors on growth, ultrasound, and carcass traits of Canadian heavy lambs. Hot carcass weight (HCW, kg), fat depth at the GR site (FATGR, mm), average carcass conformation score (CONF, points), and total carcass value (PRICE, $CAD) were measured for 8,865 purebred lambs marketed through Quebec’s Heavy Lamb Sales Agency. Corresponding management information and growth trait records for over 19,000 animals with carcass records and their relatives were extracted from the Canadian Sheep Genetic Evaluation System. Single-trait mixed linear models in SAS were used to test the significance (P < 0.05) of various non-genetic effects, after a Scheffe adjustment for multiple comparisons. All models included categorical fixed effects of sex (male or female), breed (Hampshire, HA; Suffolk, SU; Canadian Arcott, CD; Polled Dorset, DP; Rideau Arcott, RI; Polypay, PO), dam age at parity (1 to 7+ years), and birth and rearing type (born as single, twin, or triplet and more, and reared as single or multiple), and a random effect of contemporary group. Linear covariates of slaughter age or carcass weight were included in the carcass trait models, while a scanning weight covariate was used for ultrasound trait models. Male lambs were found to be significantly heavier during growth, had greater HCW and PRICE, and lower FATGR and CONF than female lambs. As expected, terminal breeds (HA, SU, CD) tended to have greater growth, greater HCW and PRICE, and lower FATGR than maternal (DP, RI, RV, PO) breeds. This information could be utilized by Canadian sheep producers to manage their flocks to maximize the revenue of lambs marketed through price grid classification systems.

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