A binomial logit function was assumed, with the test including the effect of intra- versus inter-specific treatments and the particular pathogen genotype combinations nested within the main effect.
33 Our response variable, the proportion of the population with at least one UMA in each cell, was modelled using a three-level binomial logit link model with allowances made for varying cell denominator populations.
As the data is proportional, for analysis we did generalised linear mixed effects models (GLMM) with binomial (logit-link) errors to estimate fixed differences between the P and F1 generation at each environment or to estimate fixed differences between environments at the F1 generation [ 104].
The multivariate analysis used a logistic regression model with an underlying binomial distribution with the Logit link.
Analysis of the proportion of female offspring per log applied a binomial distribution with the logit link function and a heterogeneous compound-symmetry variance covariance matrix.
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 probability distribution specified was binomial with logit link function with independent working correlation matrix.
We fitted the generalized longitudinal models using the logit link and binomial errors with the GENMOD procedure in SAS.
These case studies required animal models with Gaussian, Binomial (with logit link), Poisson, and zero-inflated Poisson (with log link) likelihoods.
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).
