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Lastly, Table 5 and 6 show the results of the Mixed Model methods treating time as continuous and categorical, respectively.
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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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].
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