Topic
Mixed model
About: Mixed model is a research topic. Over the lifetime, 3367 publications have been published within this topic receiving 159129 citations. The topic is also known as: mixed effects model.
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TL;DR: It is concluded that it is necessary to include mixed models in the estimation of breeding values in order to eliminate their influence, which significantly affects the variation of important traits for selection.
Abstract: The aims of this research were to compare estimates of variance components using different animal models and to determine the most suitable mixed model for estimating genetic parameters and genetic trends for traits in performance test of gilts of different breeds using REML. A total of 73129 gilts of four genotypes in the period of 2009 to 2013 were included in the analyses. Four mixed models were constructed. Information criterion of Akaike (AIC) and Bayesian information criterion (BIC) were used to suggest which model is an adequate model for evaluation of genetics parameters. With the introduction of certain factors in the models, reduction in components of variance and heritability in all studied traits was observed. Heritability traits in four genotypes and models were at medium to high degree of heritability. The resulting genetic trends were different between the models and the coefficients of determination (R2) were relatively high. Average gain and meat percentage were established positive (favourable) or negative (unfavourable) genetic trends in all models, while back fat thickness and lateral back fat thickness in all models were established to have positive (unfavourable) genetic trends. Based on the obtained results in this study, it is concluded that it is necessary to include mixed models in the estimation of breeding values in order to eliminate their influence, which significantly affects the variation of important traits for selection. In addition, with the inclusion of a greater number of parameters in mixed models, the models become more accurate and provide more accurate assessment of genetic and breeding value.
01 Jan 2017
TL;DR: In this paper, the authors examined a common mixed model in animal production, the model with repeated records and the most widely used mixed model for genetic evaluation in genetic evaluation, and they also introduced the concept of multitrait models.
Abstract: In a classical context, a ‘mixed model’ consists of a set of fixed effects and covariates plus one or more random effects, plus a random error term. In Chap. 1, Sect. 1.5, we have explained the differences between fixed and random effects in a frequentist context. However, as we said in Chap. 6, in a Bayesian context, all effects are random; thus, there is no distinction between fixed models, random models or mixed models. Nevertheless, we keep the nomenclatures ‘fixed’ and ‘random’ for the effects that are considered so in a frequentist model, because it is widely extended and it facilitates the understanding of the model. Later we will see which type of Bayesian random effects are what we call ‘fixed’ effects in the frequentist school. We will also consider here that the data are normally distributed, although other distributions of the data can be considered, and the procedure would be the same. In this chapter, we examine a common mixed model in animal production, the model with repeated records and the most widely used mixed model in genetic evaluation. We end the chapter with an introduction to multitrait models.
TL;DR: In this paper, a distribution-free least square approach is proposed for the analysis of correlated data arising from repeated measurements, which does not require the specifications of the distributions and initial correlation input.
Abstract: Mixed effect models, which contain both fixed effects and random effects, are frequently used in dealing with correlated data arising from repeated measurements (made on the same statistical units). In mixed effect models, the distributions of the random effects need to be specified and they are often assumed to be normal. The analysis of correlated data from repeated measurements can also be done with GEE by assuming any type of correlation as initial input. Both mixed effect models and GEE are approaches requiring distribution specifications (likelihood, score function). In this article, we consider a distribution-free least square approach under a general setting with missing value allowed. This approach does not require the specifications of the distributions and initial correlation input. Consistency and asymptotic normality of the estimation are discussed.
10 Jan 2014
TL;DR: In this paper, the authors reviewed the differences in the model assumptions in a simple setting and concluded that although popular computing solutions exist, fundamental open questions remain that impact interpretation, and proposed solutions to this problem are reviewed focusing on differences in model assumptions.
Abstract: The main objective in a group randomized trial is a comparison between treatments. Often, interest exists in the expected response for a particular group receiving a treatment. As a random sample of groups is included in the trial, the parameter for such a group is represented as a random effect. In this context, predicting the expected response for a group requires predicting a random effect. Proposed solutions to this problem are reviewed focusing on differences in the model assumptions in a simple setting. The author concludes that although popular computing solutions exist, fundamental open questions remain that impact interpretation.
Keywords:
mixed models;
random effects;
variance components;
super-population models;
model based;
design based;
Bayesian estimation;
empirical Bayes