Since both response variables are proportions, we used the binomial error distribution with the logit link in all following generalized linear mixed effects models (GLMMs).
The association between the likelihood of reporting a symptom and variables at initial classification (i.e., age at initial classification, sex, race, education, income, fatigue severity, wellness score, illness duration, onset type, and age at onset) was determined by using the binomial distribution with the logit link.
The statistical analyses used binomial (logit link function), Poisson (log link function), and gamma (log link function) error terms for the outcome of encounters (oviposition vs. rejection), frequencies of behaviours, and handling times, respectively.
The data are used to estimate binomial logit regression models for one-way and round-trip carsharing.
Finally, the presence or not of a psychological or social diagnosis was analysed using a binomial logit model.
We used the logit function with a binomial distribution [ 29] to describe the proportion of dauer larvae (p) and arrested L4 non-dauer larvae (q) among lines, ascarosides/pheromone and food treatments, and sample size (n) per plate.
We have used the logit function, which is often used as a link when the assumed distribution is binomial (Dobson 2002).
The response variable (number of sailfish in the aggregate capture) is assumed as binomial using the logit link function.
We fitted the generalized longitudinal models using the logit link and binomial errors with the GENMOD procedure in SAS.
To quantify associations between air pollutant exposures and early childhood respiratory morbidity, we fit generalized linear longitudinal models using the logit link and binomial errors.
