Contents
Can a random effect be continuous variable?
First you CANNOT treat a continuous variable as a random effect. So if you are putting area or temperature or body size is in they may be a nuisance/control variable but they are a fixed effect. Of course you are only estimating one parameter (the slope) so there is no degree of freedom cost to treating it as random.
Are random effects covariates?
Just like each fixed term in the model, each random term is made up of a random factor and a random effect. We use the term “random factor” and not “random variable” because random variables in a mixed model MUST be categorical. They are never covariates.
How are mixed effects models different from linear models?
Multiple Sources of Random Variability. Mixed effects models—whether linear or generalized linear—are different in that there is more than one source of random variability in the data. In addition to patients, there may also be random variability across the doctors of those patients.
Is it important to specify fixed and random factors in mixed models?
One of the difficult decisions to make in mixed modeling is deciding which factors are fixed and which are random. Correctly specifying the fixed and random factors of the model is vital to obtain accurate analyses.
Is the interaction with a random factor fixed or random?
Any interaction with a random factor is also random. How the factors of a model are specified can have great influence on the results of the analysis and on the conclusions drawn. One of the hardest parts of mixed models is understanding which factors to make fixed and which to make random. Learn the important criteria to help you decide.
Where to specify covariance structures in mixed models?
That is actually a difficult topic because there are two different places in mixed models where you specify covariance structures–the G matrix and the R matrix. Unfortunately, the structures that make sense in one don’t make sense in the other.