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Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.


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Journal ArticleDOI
TL;DR: Simulations were used to study the influence of model adequacy and data structure on the estimation of genetic parameters for traits governed by direct and maternal effects and showed that the lack of connectedness affects estimates when flocks have different genetic means.
Abstract: Simulations were used to study the influence of model adequacy and data structure on the estimation of genetic parameters for traits governed by direct and maternal effects. To test model adequacy, several data sets were simulated according to different underlying genetic assumptions and analysed by comparing the correct and incorrect models. Results showed that omission of one of the random effects leads to an incorrect decomposition of the other components. If maternal genetic effects exist but are neglected, direct heritability is overestimated, and sometimes more than double. The bias depends on the value of the genetic correlation between direct and maternal effects. To study the influence of data structure on the estimation of genetic parameters, several populations were simulated, with different degrees of known paternity and different levels of genetic connectedness between flocks. Results showed that the lack of connectedness affects estimates when flocks have different genetic means because no distinction can be made between genetic and environmental differences between flocks. In this case, direct and maternal heritabilities are under-estimated, whereas maternal environmental effects are overestimated. The insufficiency of pedigree leads to biased estimates of genetic parameters.

125 citations

Journal ArticleDOI
TL;DR: Adopting an exploratory data analysis viewpoint, diagnostic tools based on conditional predictive ordinates that conveniently get tied in with Markov chain Monte Carlo fitting of models are developed.
Abstract: SUMMARY In this paper, we propose a general model-determination strategy based on Bayesian methods for nonlinear mixed effects models. Adopting an exploratory data analysis viewpoint, we develop diagnostic tools based on conditional predictive ordinates that conveniently get tied in with Markov chain Monte Carlo fitting of models. Sampling-based methods are used to carry out these diagnostics. Two examples are presented to illustrate the effectiveness of these criteria. The first one is the famous Langmuir equation, commonly used in pharmacokinetic models, whereas the second model is used in the growth curve model for longitudinal data.

125 citations

Journal ArticleDOI
TL;DR: The results point to the fact that systems of farming that are more extensive and less environmentally degrading remain those most likely to participate in the REPS, and the effects of the farm- and farmer-specific characteristics may be overestimated.
Abstract: Previous studies that have attempted to model the participation decision of farmers in agri-environmental schemes have used a static framework where it was not possible to examine changes in the participation decision of farmers over time. This is rectified in this paper by utilising an 11-year panel that contains information on 300 farmers for each year. The structure of this dataset allows us to employ discrete time duration random effects panel data logit models to model the determinants of entering the Irish Rural Environment Protection Scheme (REPS). We introduce a dynamic element into a number of the models by using the random effects logit model estimator, with lagged dependent variables as additional explanatory variables. The results point to the fact that systems of farming that are more extensive and less environmentally degrading remain those most likely to participate in the REPS. In addition, the results highlight the fact that where no attempt is made to control for unobserved heterogeneity or path dependency the effects of the farm- and farmer-specific characteristics may be overestimated.

125 citations

Journal ArticleDOI
TL;DR: A full likelihood approach to estimate parameters from the linear mixed effects model for left-censored Gaussian data with application to HIV RNA Levels showed that the proposed estimators are less biased than those obtained by imputing the quantification limit to censored data.
Abstract: The classical model for the analysis of progression of markers in HIV-infected patients is the mixed effects linear model. However, longitudinal studies of viral load are complicated by left censoring of the measures due to a lower quantification limit. We propose a full likelihood approach to estimate parameters from the linear mixed effects model for left-censored Gaussian data. For each subject, the contribution to the likelihood is the product of the density for the vector of the completely observed outcome and of the conditional distribution function of the vector of the censored outcome, given the observed outcomes. Values of the distribution function were computed by numerical integration. The maximization is performed by a combination of the Simplex algorithm and the Marquardt algorithm. Subject-specific deviations and random effects are estimated by modified empirical Bayes replacing censored measures by their conditional expectations given the data. A simulation study showed that the proposed estimators are less biased than those obtained by imputing the quantification limit to censored data. Moreover, for models with complex covariance structures, they are less biased than Monte Carlo expectation maximization (MCEM) estimators developed by Hughes (1999) Mixed effects models with censored data with application to HIV RNA Levels. Biometrics 55, 625-629. The method was then applied to the data of the ALBI-ANRS 070 clinical trial for which HIV-1 RNA levels were measured with an ultrasensitive assay (quantification limit 50 copies/ml). Using the proposed method, estimates obtained with data artificially censored at 500 copies/ml were close to those obtained with the real data set.

124 citations

Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors proposed a nested linear mixed effects (LME) model including nested month-, week, and day-specific random effects of PM2.5-AOD relationships.

124 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023198
2022433
2021409
2020380
2019404