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To further explore seasonality we used a binomial response regression model.
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We parameterized the model using a binomial response distribution, logit inverse link function, and constant subject-specific random effect (detailed information on the model, estimation procedure, and code are available upon request).
We used a binomial fit for the binary response variables survival and hatching.
For all accuracy analyses we used a binomial distribution with a logistic link function.
For the binary variables, we used a binomial test.
They also expressed their physical and psychological feelings about the beverage using a 5-point scale (1 = not at all to 5 = extremely) and indicated their purchase intent using a binomial (yes/no) response.
I determined if there was a block or row location (edge/interior) effect on nut loss attributed to birds or non-bird factors pre- and post-harvest using a generalized linear model with a binomial response.
Differences in total proportions of infected plants between the treatment and control groups, and between lines, were assessed using single and mixed effects generalized linear models and corresponding analyses of variance, assuming a binomial response and with the respective addition or subtraction of 0.5 for 0 or total infections.
Response rates were summarized as a proportion including approximate 90%% confidence intervals (CI) and compared to the null hypothesis response rate using a binomial exact test.
We modelled the binary response variable infection status using a binomial distribution with a logit link function.
We therefore decided to use a binomial yes/no scoring system for the initial quantification of the angiogenic response.
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