Hi all,

Firstly, sorry if this is an easy one - I’m new to R and bayesian modelling.

I have run a brms model looking at the effect of a number of factors on playful behaviour in children and now would like to translate the output into a real scale. So for example, if I have a categorical predictor variable, like mother status (dead or alive), I want to write the results as: “children whose mother is alive are xx% more playful than those whose mother is dead”.

Similarly, for a continuous variable such as height, I want to be able to work out how a 1cm increase in height affects playfulness.

All of the non-binary variables in the model are standardized and I used the beta_binomial2 custom family.

The results of my brms model are as follows (I hope this will be enough to help me answer this!).

Family: beta_binomial2

Links: mu = logit; phi = identity

Formula: playfulness| vint(totalinteractions) ~ me(relatedness_c, relatedness.sd) + height + mother.status + group.size + Age + (1 | ID) + (1 | Year)

Data: child.data (Number of observations: 625)

Samples: 4 chains, each with iter = 6000; warmup = 3000; thin = 1;

total post-warmup samples = 12000

Group-Level Effects:

~ID (Number of levels: 57)

Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS

sd(Intercept) 0.61 0.09 0.46 0.80 1.00 2911 5587

~Year1 (Number of levels: 35)

Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS

sd(Intercept) 0.47 0.09 0.32 0.68 1.00 3256 6078

Population-Level Effects:

Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS

Intercept -5.88 0.15 -6.18 -5.58 1.00 2459 4494

height 0.10 0.05 0.01 0.20 1.00 9511 9579

mother.status -0.33 0.13 -0.57 -0.08 1.00 6913 7777

group.size -0.13 0.09 -0.31 0.04 1.00 3569 6087

Age -0.49 0.10 -0.69 -0.30 1.00 3172 5747

n.photo 0.07 0.05 -0.04 0.17 1.00 7714 8232

Relatedness -0.09 0.12 -0.32 0.13 1.00 2249 5441

Family Specific Parameters:

Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS

phi 183.13 14.89 154.86 213.41 1.00 8913 7590

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).

Thanks!

- Operating System: Windows 10
- brms Version:2.15.0