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What you are observing here is an example of the marginal versus conditional interpretation of the fixed effects coefficients from generalized linear mixed-effects models (GLMMs). Namely, in GLMMs the fixed effects have an interpretation conditional on the random effects. For your particular model, and because you have random intercepts only the fixed ...


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The fixed-effects coefficients have the interpretation of log odds ratios (and log odds for the intercept). For example, the interpretation for the coefficient of speciesTUTI is the log odds ratio for the level TUTI versus the reference level for the factor variable species. However, note that the inclusion of the random intercepts terms complicates to a ...


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You could consider a zero and one inflated Beta mixed-effects model. If we denote your outcome by $Y$, this will be the combination of a multinomial regression for $\{Y = 0\}$, $\{Y \in (0, 1)\}$ and $Y = 1$, and a Beta model for the middle part. This is model available in the brms package in R; for more info check here.


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One can view the marginal model as providing crude estimates of the regression coefficients [i.e. unadjusted for subjects] while the conditional model has regression coefficients that are assumed common to subjects and so the estimates are adjusted for subjects. In this sense, one is asking whether the subjects at hand are confounding. This is rather typical ...


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