Hi Stan-forum!

I am quite new to Bayesian modeling but have venduered out into multivatiate-hierartichael Bayesian modeling using the brms package in R (version 2.13.5).

I have searched for hours now on my issue but could not find the answer. This might be due to me not knowing the right terms well enough. In that case, I apologize in advance.

I used the brms package to model the effect of three experimental variables on my response variable (replicate devirgence). The variables are selection (yes, no), nutrient (high, low) and time (17 timepoints). I had 3 comparisons within each group which I included in the random-effects (1|comp) in addition to having the variation dependent on time. My formula looks like this:

```
brm(bf(
replicate divergence ~ time * selection * nutrient + (1 | comp),
sigma ~time * disturbance)),
family= gaussian(),
data=d, iter = 4000, warmup = 2000, prior = prior_normal, control = list(adapt_delta = 0.99))
```

I now want to plot the model with confidence intervals given only time and selection as visual components. I have managed this using add_fitted_draws() from tidybayes, however this funtion includes all the random effects as well. When I use stat_lineribbon() in the plot the mean line is not smooth like a gaussian model should be.

Does anyone know how to draw fitted_draws while excluding the random effects?

These are my variables/eastimateted parameters:

```
get_variables(m_p1_b)
[1] "b_Intercept" "b_sigma_Intercept" "b_time" "b_selectionD"
[5] "b_nutrientL" "b_time:selectionD" "b_time:nutrientL" "b_selectionD:nutrientL"
[9] "b_time:selectionD:nutrientL" "b_sigma_time" "b_sigma_selectionD" "b_sigma_time:selectionD"
[13] "sd_comparison__Intercept" "r_comparison[CDH_replica1_2,Intercept]" "r_comparison[CDH_replica1_3,Intercept]" "r_comparison[CDH_replica2_3,Intercept]"
[17] "r_comparison[CDL_replica1_2,Intercept]" "r_comparison[CDL_replica1_3,Intercept]" "r_comparison[CDL_replica2_3,Intercept]" "r_comparison[DCH_replica1_2,Intercept]"
[21] "r_comparison[DCH_replica1_3,Intercept]" "r_comparison[DCH_replica2_3,Intercept]" "r_comparison[DCL_replica1_2,Intercept]" "r_comparison[DCL_replica1_3,Intercept]"
[25] "r_comparison[DCL_replica2_3,Intercept]" "lp__" "accept_stat__" "stepsize__"
[29] "treedepth__" "n_leapfrog__" "divergent__" "energy__"
```

And this is the model:

```
Family: gaussian
Links: mu = identity; sigma = log
Formula: value ~ time * selection * nutrient + (1 | comparison)
sigma ~ time * selection
Data: d (Number of observations: 106)
Samples: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
total post-warmup samples = 8000
Group-Level Effects:
~comparison (Number of levels: 12)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.02 0.01 0.00 0.05 1.00 1784 3586
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.72 0.03 0.67 0.77 1.00 3422 5429
sigma_Intercept -2.41 0.10 -2.61 -2.19 1.00 9740 5814
time -0.02 0.00 -0.02 -0.01 1.00 4957 4996
selectionD -0.24 0.05 -0.34 -0.14 1.00 4943 4995
nutrientL -0.04 0.03 -0.11 0.03 1.00 2496 4410
time:selectionD 0.03 0.00 0.02 0.04 1.00 5136 6227
time:nutrientL 0.01 0.00 -0.00 0.01 1.00 5318 4802
selectionD:nutrientL 0.16 0.07 0.03 0.29 1.00 4331 5770
time:selectionD:nutrientL -0.01 0.01 -0.02 0.00 1.00 5522 5713
sigma_time 0.06 0.01 0.03 0.09 1.00 4770 5658
sigma_selectionD 0.63 0.15 0.35 0.92 1.00 9900 5355
sigma_time:selectionD -0.12 0.02 -0.15 -0.08 1.00 4301 5818
Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
```

Thank you in advance,

Best regards Maddie