Estimating marginal effects of offset in brms Poisson regression model


[I posted this question already on Stack Overflow, but was advised I might get more response if posted here]

I am learning Bayesian regression modelling using the brms package in R.

I am modelling rates of infection per head of population at the neighbourhood level, and investigating associations with other neighbourhood-level covariates such as poverty level and distance to a health centre.

For example:



    df <- tibble(neighbourhood = seq(1:200),
             cases = rpois(n = 200, lambda = 3),
             population = round(runif(n = 200, min = 100, max = 10000)),
             poverty = round(runif(n = 200, min = 30, max = 90)),
             distance = runif(n = 200, min = 20, max = 10000))

Using this made up dataset, I can construct a Bayesian regression model (my real model is more complex, with spatial autocorrelation terms and other covariates).

Note the offset(log(population)) term to allow adjustment for neighbourhood population size.

prior <- c(prior_string("normal(0,10)", class="b"),
           prior_(~normal(0,10), class= ~Intercept))

m1 <- brm(bf(cases ~
          poverty +
          log(distance) +
          prior = prior,
          iter=4000, warmup=1000,


Family: poisson 
Links: mu = log 
Formula: cases ~ poverty + log(distance) + offset(log(population)) 
Data: df (Number of observations: 200) 
Samples: 3 chains, each with iter = 4000; warmup = 1000; thin = 1;
     total post-warmup samples = 9000

Population-Level Effects: 
            Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept      -7.40      0.35    -8.08    -6.71       6591 1.00
poverty         0.01      0.00     0.00     0.01       9000 1.00
logdistance    -0.04      0.04    -0.12     0.03       5936 1.00

Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample 
is a crude measure of effective sample size, and Rhat is the potential 
scale reduction factor on split chains (at convergence, Rhat = 1).

I can plot the marginal effects of the poverty and log(distance) variables by running


I understand that the marginal effects plots are estimated at the mean value of other covariates within the model.

What I am really interested in however, is plotting the number of cases as a function of population size, at the mean levels of distance and poverty.

Even better would be plots of rates of infection per head of population as functions of distance and poverty.

Not sure if what I am looking for either a) makes sense, or b) is possible with brms… but very grateful for any suggestions.

  • Operating System: macOS High Sierra, Version 10.13.3
  • brms Version: 2.3.1


marginal_effects currently doesn’t treat offset variables as variables that can be plotted. That could be changed but I wonder what the benefit of this would be given that we already know the size of the relationhip since we modeled it as known.


Thanks for the response Paul - much appreciated!

For these sorts of analyses, epidemiologists usually want to see in plots the “effect” of covariates on e.g. the rate of infection per 100,000 people. Currently, with y-axes being in counts (rather than rates), it is more difficult to intuitively see the size of the relationship.

Hope his makes sense!



In this case, you can do the following:

me <- marginal_effects(m1, conditions = data.frame(population = 100000))
# transform me[[1]] etc. to your needs


Thanks so much! That is amazing! Really grateful for your support and this amazing package!