An Introduction to (Generalized (Non)Linear Mixed Models

2004 ◽  
pp. 111-153 ◽  
Author(s):  
Geert Molenberghs ◽  
Geert Verbeke
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Non Linear

QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato

2006 ◽  
Vol 113 (2) ◽  
pp. 288-300 ◽  
Author(s):  
M. Malosetti ◽  
R. G. F. Visser ◽  
C. Celis-Gamboa ◽  
F. A. van Eeuwijk
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Response Curves ◽  
Non Linear

scholarly journals Non-linear mixed models in the study of growth of naturalized chickens

2020 ◽  
Vol 49 ◽  
Author(s):  
Vicente Ibiapina ◽  
Firmino José Vieira Barbosa ◽  
José Elivalto Guimarães Campelo ◽  
José Lindenberg Rocha Sarmento
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Non Linear

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 Ability of non-linear mixed models to predict growth in laying hens

2014 ◽  
Vol 43 (11) ◽  
pp. 573-578 ◽  
Author(s):  
Luis Fernando Galeano-Vasco ◽  
Mario Fernando Cerón-Muñoz ◽  
William Narváez-Solarte
Keyword(s):  
Mixed Models ◽  
Laying Hens ◽  
Linear Mixed Models ◽  
Non Linear

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring

2015 ◽  
Vol 34 (20) ◽  
pp. 2858-2871 ◽  
Author(s):  
Nele Goeyvaerts ◽  
Elke Leuridan ◽  
Christel Faes ◽  
Pierre Van Damme ◽  
Niel Hens
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Maternal Antibody ◽  
Non Linear ◽  
Disease Analysis
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