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Results were analyzed with mixed models to test the fixed effect of treatment.
This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data.
We use spatial Generalized Linear and Mixed Models to account for non-independence (spatial autocorrelation) between neighboring census block groups.
We subsequently fit linear mixed models to each expression trait (20,400) and putative QTL position.
For longitudinal analyses over the first week, we used regression analysis with mixed models to account for correlation of these markers over time.
To compare the diving behaviour between the three age classes, we used General Linear Mixed Models to avoid pseudo replication [32].
We fitted several mixed models to the data.
We used Pearson's correlation coefficient and linear mixed models to address other objectives.
We used adjusted general linear mixed models to analyze functioning and HRQoL.
For our analysis we will use mixed models to estimate the effects of factors and interactions.
We shall use mixed models to analyse the continuous variables and these will include baseline covariates.
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