The concept of crash generation models (CGMs) is introduced utilizing trip generation data in a generalized linear regression with the assumption of a negative binomial error structure.
There are also several approaches that utilize either a linear or a generalized linear modeling framework for a gene set analysis.
Time-to-event statistics (pseudo-observation method through a generalized linear regression model) will be utilized to analyze the association between running experience or running pace and the risk of RRI after 50 km of follow-up.
A generalized linear mixed-effect model was utilized to identify independent predictors of mortality.
Damage as a function of treatment (3 levels) was analyzed using a generalized linear model with mixed effects, utilizing the glmmPQL command in the MASS package (R 2.8.1, R Foundation for Statistical Computing, 2008).
A generalized linear Poisson regression model with natural cubic splines was utilized for the single-day lag effects of rainfall after examining the shapes of each exposure outcome relation to confirm assumptions regarding linearity.
Differences between HbA1c groups were analyzed using a generalized linear model (GLM), with differences between groups tested by utilizing z-statistics.
Treatment differences were analyzed using a generalized linear mixed model.
Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM).
A generalized linear model (glm) fit gave almost identical results (Figure 1).
