Which is a random variable in a mixed model?
The g1 variable is random, which results in a mean intercept and a standard deviation for the intercept. There are also two fixed continuous variables, x1 and x2. This provides a fixed slope for each, although the slope for x1 may be 0. Adding a random slope for x2 will allow for different x2 slopes for each group in g1.
Can a random slope be included in a categorical predictor?
If you include a random slope for a categorical predictor, and there is only one response per subject per category, then that random slope becomes confounded with the residual. But you can include a quadratic term for time, if the non-linearity fits a curve of that time. If I had more time points, I could try other non-linear terms.
How are X1 and X2 variables used in a mixed model?
There are also two fixed continuous variables, x1 and x2. This provides a fixed slope for each, although the slope for x1 may be 0. Adding a random slope for x2 will allow for different x2 slopes for each group in g1. These random slopes may or may not be correlated with the random intercepts already associated with g1.
When do you add a random slope to a GMM model?
Random slopes. When a slope is random, the intercept may or may not be random as well. The gmm model, from prior articles, includes a random intercept which we accepted as significant. We will add a random slope for the x2 variable to the gmm model.
How to write random slope models in lme4?
That is where random slope models come into play. Writing up the model with both random intercept (Intercept is always 1) and random slope (attitudepol) for a subject at the same time in lme4 package is very intuitive, you just add + them: (1+attitude|subject). Models with random slopes automatically model random intercepts as well.
How are random slopes used in a model?
Models with random slopes automatically model random intercepts as well. Thus you might see syntax like that: (attitude|subject), where Intercept 1+ is included implicitly. Now the slope (attitudepol) also changes for every subject, which makes the model more realistic.