Multivariate models with tidybayes

Is the tidybayes package capable of handling multivariate models? Looking at the tutorials, the walkthroughs are all for bivariate models.

I have a multivariate model, and I would like to get the posterior estimates for a single IV in the model equation. For my example, I’ll choose AgeGroups4.

I have the following model estimated using brms:

model1 <- brm(Q3 ~ AgeGroups4 + 
                Gender + 
                Education + 
                INCOME_5 + 
                IncomeFeelings + 
                EMP_2010 + 
                HouseholdSize + 
                Urbanicity + 
                CC.EST_gmc + 
                GE.EST_gmc + 
                PV.EST_gmc + 
                RQ.EST_gmc + 
                RL.EST_gmc + 
                VA.EST_gmc + 
                Year + 
                (1 | Country),
              data = dataframe10PC,  
              family = categorical(link = "logit"),
              chains = 4, 
              cores = 4, 
              threads = threading(4),
              iter = 1000, 
              warmup = 500)


To get posterior estimates for AgeGroup4, my code using the tidybayes shell code would be:

age_plot <- dataframe10PC %>%
  filter(! %>%
  data_grid(AgeGroups4) %>%
                  category = c("Q3")) %>%
  ggplot(aes(x = .epred, 
             y = Q3)) +
  coord_cartesian(expand = FALSE) +
  facet_grid(. ~ AgeGroups4, 
             switch = "x") +
  theme(strip.background = element_blank(), 
        strip.placement = "outside") +
  xlab("Age group") + 

However, when I put this code in I get:

*Error: The following variables can neither be found in 'data*' *nor in 'data2*

This is followed by a string of the rest of the IVs in the model equation for model 1.

Is it possible to get the estimates for a single IV in a multivariate model using tidybayes? If so, how is this done? Note that the code works when estimated as a bivariate model (Q3 ~ AgeGroups4).

Thanks in advance.

EDIT (Aki): added codeblock formating


This is the data you are using to make the predictions:

I think that data frame is missing the relevant columns due to how you call data_grid()

The following vignette has also examples with more than one covariate