random effects model

(redirected from Variance components)

random effects model

A statistical model that may be used in meta-analysis, in which both within-study sampling error (variance) and between-studies variation are included in assessing the uncertainty or confidence interval of the results of the meta-analysis.
References in periodicals archive ?
we value new, innovative methods that accelerate computing, allow higher spatiotemporal resolution and take into account all relevant variance components.
Variance components and genetic parameters for weight and size at birth in the Boer goat.
So the objectives of this study were estimation and assessment of genetic parameters and variance components of 305-day milk yield using repeatability and different RRM.
Since no study has been done yet to estimate genetic parameters of reproductive traits in Kordi ewes of Northern Khorasan, this study aimed to estimate variance components as well as genetic parameters of reproductive traits in Kordi ewes.
Percentages of each variable were used to obtain estimates of variance components in fruit shape (individual restricted maximum likelihood [REML] analysis), predicted additive genetic effect of parents and the specific combining ability of the crosses using model 36 in Selegen-REML/BLUP (best linear unbiased prediction) software (RESENDE, 2006).
The variance components from this first analysis step state the total variation between trees before introducing tree- and stand-level variables.
Table IV illustrates summary results of variance components with regard to UN, CSH, UNR, AR(1), and TOEPH which were identified to be the best covariance structures for the RISM built with/without specifying covariates.
Inspired by these results, Chen and Petkova (2012) suggest that the factors omitted in the Fama and French (1996) model could be the aggregate market variance components, defined by average variance and average covariance.
Table 3: Estimates of relative proportion of variance components for general combining ability (GCA), specific combining ability (SCA) and reciprocal effects for quantitative traits in maize under salt conditions.
Therefore, fully parameterized multi-trait mixed-model is emerged as a flexible approach that considers both the within-trait and between-trait variance components simultaneously for multiple traits.
Estimates of variance components from single-trait analyses were used as priors to carry out two-trait analyses.