A subsequent multivariate GLM was conducted that included treatment condition, the four significant psychosocial predictors, and demographic variables.
The predictor variable for the multivariate GLM was zone, which indicated whether a population of G. fultoni was far allopatric, near allopatric, or sympatric.
For each condition, a multivariate GLM was computed with the reaction time (RT) and accuracy rate (ACC) as dependent variables and type (with or without DD), grade (3rd or 6th grade) or gender (female or male) as a fixed factor.
Specifically, multivariate GLM were performed to study the effects of treatment strategies on the total 1-year medical costs and productivity costs, while controlling for patient characteristics including age, gender, insurance plan type, Charlson comorbidity score, primary admission diagnosis, and treatment strategy.
The fitting of generalized linear models (GLMs) and the choice of designs for GLMs are discussed in the chapter.
Multivariate General Linear Models (GLMs) were used with mean values for each hour and day of DSI body temperature, activity, and heart rate as dependent variables, and day and hour as independent variables.
The multivariate GLM will be built by manually adding covariates one-by-one (forward method; sorted by descending univariate p-values), whilst observing changes to the coefficients and errors for variables already in the model.
Generalized linear models (GLMs) were then calculated.
Separate GLMs were set up for encoding and retrieval.
Three separate GLMs were carried out.
