Genetic variation of traits measured  in several environments. II. Inference on between-environment homogeneity of intra-class correlations

Robert, C.; Foulley, JL; Ducrocq, V.

doi:10.1051/gse:19950203

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…There is now a large amount of experimental evidence of heterogeneous variances for most important livestock production traits [14,33,43,44]. Major theoretical and applied work has been carried out for estimating and testing sources of heterogeneous variances arising in univariate mixed models [4,9,11,12,15,30,31,34,45].…”

Section: Introductionmentioning

confidence: 99%

Modelling the�growth curve of�Maine-Anjou beef cattle using�heteroskedastic random coefficients�models

Robert-Grani�¹,

Heude

Foulley

2002

Genet. Sel. Evol.

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-A heteroskedastic random coefficients model was described for analyzing weight performances between the 100th and the 650th days of age of Maine-Anjou beef cattle. This model contained both fixed effects, random linear regression and heterogeneous variance components. The objective of this study was to analyze the difference of growth curves between animals born as twin and single bull calves. The method was based on log-linear models for residual and individual variances expressed as functions of explanatory variables. An expectation-maximization (EM) algorithm was proposed for calculating restricted maximum likelihood (REML) estimates of the residual and individual components of variances and covariances. Likelihood ratio tests were used to assess hypotheses about parameters of this model. Growth of Maine-Anjou cattle was described by a third order regression on age for a mean growth curve, two correlated random effects for the individual variability and independent errors. Three sources of heterogeneity of residual variances were detected. The difference of weight performance between bulls born as single and twin bull calves was estimated to be equal to about 15 kg for the growth period considered. heteroskedastic random coefficient model / EM-REML / robust estimators / growth curve / Maine-Anjou breed

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Section: Introductionmentioning

confidence: 99%

Modelling the�growth curve of�Maine-Anjou beef cattle using�heteroskedastic random coefficients�models

Robert-Grani�¹,

Heude

Foulley

2002

Genet. Sel. Evol.

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“…4 (24) (24) (36) to (37). Figure 1 plots the response given in equation (36) (22) and by the likelihood based index 1 (20).…”

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confidence: 99%

Prediction of the response to a selection for canalisation of a continuous trait in animal breeding

Sancristobal-Gaudy¹,

Elsen²,

Bodin³

et al. 1998

Genet Sel Evol

121

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“…We can extend this model to take into account heterogeneous variances both at the temporal (in G matrix) and residual (in R matrix) levels [6,8,10,20,21,23,32] which enlarges the range of potentially useful models for longitudinal data analysis.…”

Section: Variance Covariance Structurementioning

confidence: 99%

Statistical analysis of somatic cell scores via mixed model methodology for longitudinal data

Robert-Granié¹,

Foulley

Maza³

et al. 2004

Anim. Res.

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-The aim of this study was to analyze somatic cell counts which is an indirect criterion. to assess susceptibility to mastitis. Data analyzed were weekly records (6448 somatic cell scores) out of 159 primiparous Holsteins and Holsteins x Normande cows raised at the INRA" Le Pin au Haras" experimental farm, France. Given the longitudinal structure of this data set, the analysis consists of modeling both the mean and the individual profiles. This was achieved via the use of mixed models including fixed effects for the average profiles and random effects for the adjusted individual profiles. As far as fixed effects are concerned, the main issue is to fit a time trend to the average profiles. For this, we employed the technique of fractional polynomials described in Royston and Altman (Appl. Stat. 43 (1994) 429-467) under several variance-covariance structures. The best second degree polynomial involved an intercept plus the time at the power (-1/3) (i.e., ) plus the latter times the logarithm of the time (i.e., × log (t)). Regarding random effects, model comparisons involved random coefficient models, exponential stationary stochastic processes and heterogeneous variances. The models that simultaneously included all these three structures turned out to be the best. For instance, random coefficient models did not fit the variance function well, even when the degree of the polynomial was high. This phenomenon partly justified the introduction of heteroskedastic models.longitudinal data / mixed models / fractional polynomials / robust estimators / somatic cell scores Résumé -Analyse statistique des scores de cellules somatiques par la méthodologie des modèles mixtes appliquée à des données longitudinales. L'objectif de cette étude est d'analyser les comptages de cellules somatiques, qui constituent un critère indirect d'appréciation de la sensibilité aux mammites. Les données analysées étaient relatives à 6448 contrôles hebdomadaires de cellules somatiques du lait effectués sur 159 génisses de race Holstein et croisées Holstein x Normande, entretenues au domaine expérimental INRA-Le pin au Haras. Eu égard à la structure longitudinale * Corresponding author: robert@germinal.toulouse.inra.fr des données, l'analyse a consisté à modéliser les profils moyens et individuels de réponse. À ce propos, ont été mis en oeuvre des modèles mixtes dont les effets fixes décrivent les profils moyens et les effets aléatoires les profils individuels. Concernant les effets fixes, on a utilisé la technique des polynômes fractionnaires, décrite par Royston et Altman [29] assortie de différentes structures de variance-covariance. Le meilleur ajustement est fourni par un polynôme de second degré comportant un terme constant, le temps à la puissance moins un tiers (i.e., ) et ce même terme multiplié par le logarithme du temps (i.e., × log(t)). Concernant la partie aléatoire du modèle, les modèles mis en comparaison faisaient appel à une régression sur le temps à coefficients aléatoires, à des processus stochastiques stationnaires e...

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Genetic variation of traits measured in several environments. II. Inference on between-environment homogeneity of intra-class correlations

Cited by 4 publications

References 6 publications

Modelling the�growth curve of�Maine-Anjou beef cattle using�heteroskedastic random coefficients�models

Modelling the�growth curve of�Maine-Anjou beef cattle using�heteroskedastic random coefficients�models

Prediction of the response to a selection for canalisation of a continuous trait in animal breeding

Statistical analysis of somatic cell scores via mixed model methodology for longitudinal data

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