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In the first stage the across array fixed effects; panel, dye, treatment and mixed model effects; array and dye within array were modeled.
Other repeated measures were analyzed in a random effects mixed model: effects of different animals, groups, BW, and amount of bleeding were included in the model aiming at achieving low information criteria.
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Shifts in functional assemblages of response and effect traits were analyzed by multivariate analyses, whereas changes in single trait metrics were analyzed by mixed-model effects.
The above equation is generally called a mixed model (or mixed effects model).
First, we constructed a linear mixed model with crossed effects.
ANOVA (mixed model) was used (effects considered significant at p<0.05).
Topics include generalized linear models for various data types and their extensions, such as discrete choice models, survival outcome models, mixed effects and multilevel models.
Data were analyzed using mixed models with random patient effects.
GLMMs: Generalized linear mixed models; LMEs: Linear mixed effect models; AIC: Akaike information criterion.
Mixed effects models and log binomial models were used to assess the association of maternal PHIV status with infant outcomes.
It was a mixed effect model and could be modelled with the R package nlme [55].
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