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This is a generalized linear model with binomial distribution family and identity link function [ 32].
We used generalized linear mixed-effect models (GLMM) with binomial distributions to analyze our data set.
The models were fit using the GENMOD procedure (with binomial distribution and identity link) in SAS 9.1 (Cary, NC, USA).
Binary data with binomial distribution, Binomial(n = 1, P = 0.5).
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.
Fruit choice was analyzed using a logistic regression model with a binomial distribution (the sum of seven binary outcomes).
Data on earthworm mortality and change in body mass during the experiment were analyzed by a generalized linear model with binomial error distribution and linear model, respectively in both cases with the type of microplastic as predictor variable.
Infection patterns were analyzed using a logistic regression model with binomial error distribution and 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).
Once again, the models with negative binomial distribution had a lower AIC and therefore a better estimate than the models which assumed a Poisson distribution.
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