I’m fitting a mixed effect model in rstanarm of the form
m1<-stan_glmer(y~gender+country+ rcs(age) + rcs(AnotherContinousVar) + (1|subjectID),family="binomial")
I’d like to plot the relationship between the restricted cubic splines of each continous var VS the predicted probabilities. But I’m not sure I’m doing it correctly. Here’s what I do
First, create predictions on the same data using posterior_linpred(m1,transform=TRUE) which gives me predicted probabilites.
Next, I take the mean of these probabilities for each data point and store them in the original df so as to match the original datapoints
preds<-posterior_linpred(m1,transform=TRUE) pred<-colMeans(preds) dta%>% mutate(probability=pred)%>% ggplot(.,aes(Age,probability))+geom_smooth(method = "loess")
What I’m confused about is
- Is this the probability given Age only or is this the Pr given Age AND all other factors ? If so, what level are those factors at? Are these marginal effects or conditional?
- Is loess smoothing the appropriate way of visualizing the relationship between the continous var and the predicted probabilities?
- Is there another way that I can visualize the relationship and the uncertainty of the continous var vs probabilities but also, how do I interpret them.
- Should I use something like
r expand.grid()to create all possible combinations and then get predicted probabilities on those data points instead?
Another thing I’m still not clear about is, subjects are treated as random effect with a separate intercept for each (mutliple response by each subject). Do the predicted probabilities take into account the variance explained by these random effects?