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To identify the variance components for the random effects we used the linear mixed model formula lmer (implemented in the LME4 library) [47].
It was fitted with the linear mixed model formula lme (implemented in the NLME library) in the R environment (version 2.11.1) [45] and model parameters were estimated with maximum likelihood.
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Variable model estimation consisted of a linear model, logarithmic model, and mixed model depending on which formula was selected for the health-related indicators.
Effect sizes (Cohen's d) were calculated with the estimated means from the mixed model and by the following formula for converting standard error to standard deviation: SD = SE* sqrt(n)).
The r2 from the linear mixed model was calculated using the sem.model.fits function based on the marginal r2 formula of Nakagawa and Schielzeth68.
The variance formula for the general index was derived using a linear mixed model, with statistical tests and confidence intervals constructed assuming Gaussian-distributed observations.
DESIGN: Between-groups mixed model.
Generalized linear mixed model.
Multi-locus mixed model.
linear mixed model analysis.
Generalized additive mixed model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com