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Again, logit models based on binomial distributions (z-scores, Generalized Linear Mixed Model) were used to model binary decisions [33] by our listener responses (i.e., the listeners' /ɡ/ or /k/ response).
Two of them, namely the strongest component model and a proposed perceptual version of the mixed model, were able to better predict total annoyance than the other tested models.
First, the same predictors as for the mixed model, were tested univariably.
Post hoc analyses (linear mixed model) were performed within groups in the case of significant interactions.
Analyses of repeated measurements with the general linear mixed model were performed in SAS 8.2 (Cary, NC, USA).
To perform smoothing, raw P values for each marker from the mixed model were sorted by chromosome location.
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So the mixed model is superior to each sub-model.
A mixed model was used to evaluate the predictor variables.
A mixed model is also likely.
The mixed model is used since CEL files are random factors.
To analyze data from human plasma, a linear mixed model was used.
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