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Model-based inversion (Russell and Hampson 1991) uses a generalized linear inversion algorithm (GLI), which attempts to modify the initial model until the resulting synthetic matches the seismic trace within some acceptable limit.
EdgeR uses a generalized linear model (GLM) to identify differential enrichment by fitting the genomic count data to a negative binomial distribution.
The method first clusters nearby predicted binding sites into consensus sites across multiple conditions, and then uses a generalized linear model with Negative Binominal distribution to detect differential TF binding across samples.
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Effects were tested using a generalized linear mixed effects model with a Poisson error distribution (log link).
Treatment differences were analyzed using a generalized linear mixed model.
Data was analyzed using a generalized linear mixed model, acknowledging the spatial distribution of data.
Because of the non-normal distribution of the data, even after transformations, we performed analysis using a generalized linear model with a single factor (i.e., species) using a Poisson distribution unless otherwise stated.
In the first step, we used a generalized linear mixed-effects model to assess the variables that influence macroptery.
The influence of grazing intensity on change indicators was tested using a generalized linear regression.
We used a generalized linear mixed model (GLMM) with binomial errors and a logit-link function.
The primary outcome will be modelled using a generalized linear mixed-model for repeated measures based on a participant-level analysis.
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