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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: Hierarchical linear models have found widespread application when the data have a nested structure, such as when students are nested within classrooms (a two-level nested structure) or students....
Abstract: Hierarchical linear models have found widespread application when the data have a nested structure—for example, when students are nested within classrooms (a two-level nested structure) or students...

224 citations

Journal ArticleDOI
TL;DR: The case of nonhomogeneous covariate regressions in the mixed model is considered in the context of interpreting predicted future differences among levels of a given factor or interaction, and the question of whether the regressions are homogeneous is itself often of substantive interest.
Abstract: The model generally considered in analysis of covariance has all levels of classification factors and interactions fixed, and also covariate regression coefficients fixed. Mixed models are more appropriate in most applications. A summary of estimation and hypothesis testing for analysis of covariance in the mixed model, including the case of random regression coefficients, is presented. Higher-level covariate regressions (i.e., regressions in which, for all levels of a factor or interaction, all observations on the same level have a common covariate value) are discussed. Nonestimability problems that result from defining such covariates at the levels of fixed effects are illustrated. The case of nonhomogeneous covariate regressions in the mixed model is considered in the context of interpreting predicted future differences among levels of a given factor or interaction. Nonhomogeneous regressions complicate interpretations only when they are associated with the contrast(s) of interest among fixed effects in the model. The question of whether the regressions are homogeneous is itself often of substantive interest. Different random regression coefficients associated with the levels of a random effect are also examined.

221 citations

Journal ArticleDOI
TL;DR: The maternal genetic variance or direct-maternal genetic covariance component, or both, was different from zero for all traits in Hampshires and Polled Dorsets, suggesting that maternal effects were important for weight of lambs even at 100 d of age.
Abstract: Variance components were estimated for lamb weight at birth, 50 d, and 100 d of age. Data from the Canadian flock recording program for lambs born in 1977 to 1991 for Hampshires (n = 6,395) and Polled Dorsets (n = 29,204) and 1982 to 1991 for Romanovs (n = 3,432) were studied. Observed weights were pre-adjusted for the effects of age of dam, sex of lamb, birth-rearing type, month or quarter of year of birth, parity-lambing interval, and age of dam at first lambing, using estimates derived from a fixed effects model including contemporary groups plus these factors. Pre-adjusting for nuisance variables reduced the number of equations in the model for variance component estimation. A single-trait animal model with derivative-free restricted maximum-likelihood procedures was used. Random effects were additive direct and maternal genetic, litter (common environmental), and error. An alternate model excluded maternal genetic effects. Estimates of litter variance as a proportion of phenotypic variance were of moderate size (.12 to .43) and consistent across breeds and models. The mean correlation between direct and maternal genetic effects, across traits and breeds, weighted by the number of animals, was -.40 (SE = .15). The maternal genetic variance or direct-maternal genetic covariance component, or both, was different from zero (P < .05) for all traits in Hampshires and Polled Dorsets, suggesting that maternal effects were important for weight of lambs even at 100 d of age. Estimates of direct heritability ranged from .05 to .45, varying across traits, breeds, and models.(ABSTRACT TRUNCATED AT 250 WORDS)

220 citations

Journal ArticleDOI
TL;DR: Several techniques that can be used to compare two independent families of lines are described tutorially, compared, and discussed in the context of more sophisticated and more naive approaches to this common data-analytic problem.
Abstract: Laboratory experiments often involve two groups of subjects, with a linear phenomenon observed in each subject. Simple linear regression as propounded in standard textbooks is inadequate to treat this experimental design, particularly when it comes to dealing with random variation of slopes and intercepts among subjects. The author describes several techniques that can be used to compare two independent families of lines and illustrates their use with laboratory data. The methods are described tutorially, compared, and discussed in the context of more sophisticated and more naive approaches to this common data-analytic problem. Technical details are supplied in APPENDIX A.

220 citations


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