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A mixed model method was used to assess trends in intake over time, and to estimate mean intake and within-subjects and between-subjects variance.
Two primary subject-specific approaches to height modeling, the dummy variable method (fixed individual effects) and the mixed model method (random individual effects), were compared using Chapman Richards type models fitted to second-rotation loblolly pine (Pinus taeda L). data from a designed experiment.
Variance components used to estimate heritability were calculated with a mixed model method, the animal model, using ASREML software [67].
Our adapted mixed model method highly enriches for SNPs within the SFP region.
Associations were further tested by the unified mixed model method for association mapping (MLM) in TASSEL [ 48].
In addition the Bayesian method provides an exact measure of error for variances and variance ratios, whereas convolute approximations are needed in the classical mixed model method.
Similar(43)
To gain further insight, we relied on mixed model methods.
A key element of our analysis was mixed model methods.
Contrary to repeated measures analysis of variance, mixed model methods analyse all available data without any data loss [ 31].
Mixed model methods were used for the analysis, with separate variances fit for each of the 2 groups (SC and C) when necessary.
We utilize mixed model methods to account for the pedigree-based structure in our sample through the use of the genomic relationship matrix [ 19- 21].
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