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We always modelled with a binomial link function and a logit error.
Relative risk of being downloaded or cited one time are modeled with a multiple negative binomial regression.
Thus, we want to examine the portion of data that lies between these two conditions, i.e. where the dispersion is low enough to model with a negative binomial but still larger than the value where the negative binomial approaches the Poisson distribution.
For competing risks analysis with covariates whose effects vary with time, Scheike and Zhang (2008) have developed models based on a binomial regression approach.
This was modeled as a binomial variable (Failure yes/no).
For each experiment we used a Generalized Linear Mixed Model with a binomial distribution with log-link function.
Larval viability data were tested with a generalized linear model with a binomial distribution and a logit link function.
For residence time we used models with a normal distribution, for local dominance we used models with a binomial distribution and a logit link, with binomial totals set to 50%% (the highest value in our dataset).
Similarly, the number of required experimental trials when using an SPAD as an idler (trigger) detector was calculated, with the SPAD's binary detection modelled by a binomial trial based on their known efficiency.
We fitted a linear mixed model using the exact binomial approach with the restricted maximum likelihood method [ 24].
Survival was modeled using a binomial error distribution.
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