Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models

Fit Bayesian generalized (non-)linear multivariate multilevel models using Stan for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distributions can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.

brm(formula, data, family = gaussian(), prior = NULL, autocor = NULL,
  cov_ranef = NULL, sample_prior = c("no", "yes", "only"),
  sparse = FALSE, knots = NULL, stanvars = NULL, stan_funs = NULL,
  fit = NA, save_ranef = TRUE, save_mevars = FALSE,
  save_all_pars = FALSE, inits = "random", chains = 4, iter = 2000,
  warmup = floor(iter/2), thin = 1, cores = getOption("mc.cores",
  1L), control = NULL, algorithm = c("sampling", "meanfield",
  "fullrank"), future = getOption("future", FALSE), silent = TRUE,
  seed = NA, save_model = NULL, stan_model_args = list(),
  save_dso = TRUE, file = NULL, ...)

An object of class formula, brmsformula, or mvbrmsformula (or one that can be coerced to that classes): A symbolic description of the model to be fitted. The details of model specification are explained in brmsformula.


An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model.


A description of the response distribution and link function to be used in the model. This can be a family function, a call to a family function or a character string naming the family. Every family function has a link argument allowing to specify the link function to be applied on the response variable. If not specified, default links are used. For details of supported families see brmsfamily. By default, a linear gaussian model is applied. In multivariate models, family might also be a list of families.


One or more brmsprior objects created by set_prior or related functions and combined using the c method or the + operator. See also get_prior for more help.


An optional cor_brms object describing the correlation structure within the response variable (i.e., the 'autocorrelation'). See the documentation of cor_brms for a description of the available correlation structures. Defaults to NULL, corresponding to no correlations. In multivariate models, autocor might also be a list of autocorrelation structures.


A list of matrices that are proportional to the (within) covariance structure of the group-level effects. The names of the matrices should correspond to columns in data that are used as grouping factors. All levels of the grouping factor should appear as rownames of the corresponding matrix. This argument can be used, among others to model pedigrees and phylogenetic effects. See vignette("brms_phylogenetics") for more details.


Indicate if samples from all specified proper priors should be drawn additionally to the posterior samples (defaults to "no"). Among others, these samples can be used to calculate Bayes factors for point hypotheses via hypothesis. If set to "only", samples are drawn solely from the priors ignoring the likelihood, which allows among others to generate samples from the prior predictive distribution. In this case, all parameters must have proper priors.


Logical; indicates whether the population-level design matrices should be treated as sparse (defaults to FALSE). For design matrices with many zeros, this can considerably reduce required memory. Sampling speed is currently not improved or even slightly decreased.


Optional list containing user specified knot values to be used for basis construction of smoothing terms. See gamm for more details.


An optional stanvars object generated by function stanvar to define additional variables for use in Stan's program blocks.


(Deprecated) An optional character string containing self-defined Stan functions, which will be included in the functions block of the generated Stan code. It is now recommended to use the stanvars argument for this purpose, instead.


An instance of S3 class brmsfit derived from a previous fit; defaults to NA. If fit is of class brmsfit, the compiled model associated with the fitted result is re-used and all arguments modifying the model code or data are ignored. It is not recommended to use this argument directly, but to call the update method, instead.


A flag to indicate if group-level effects for each level of the grouping factor(s) should be saved (default is TRUE). Set to FALSE to save memory. The argument has no impact on the model fitting itself.


A flag to indicate if samples of latent noise-free variables obtained by using me and mi terms should be saved (default is FALSE). Saving these samples allows to better use methods such as predict with the latent variables but leads to very large R objects even for models of moderate size and complexity.


A flag to indicate if samples from all variables defined in Stan's parameters block should be saved (default is FALSE). Saving these samples is required in order to apply the methods bridge_sampler, bayes_factor, and post_prob.


Either "random" or "0". If inits is "random" (the default), Stan will randomly generate initial values for parameters. If it is "0", all parameters are initialized to zero. This option is sometimes useful for certain families, as it happens that default ("random") inits cause samples to be essentially constant. Generally, setting inits = "0" is worth a try, if chains do not behave well. Alternatively, inits can be a list of lists containing the initial values, or a function (or function name) generating initial values. The latter options are mainly implemented for internal testing.


Number of Markov chains (defaults to 4).


Number of total iterations per chain (including warmup; defaults to 2000).


A positive integer specifying number of warmup (aka burnin) iterations. This also specifies the number of iterations used for stepsize adaptation, so warmup samples should not be used for inference. The number of warmup should not be larger than iter and the default is iter/2.


Thinning rate. Must be a positive integer. Set thin > 1 to save memory and computation time if iter is large.


Number of cores to use when executing the chains in parallel, which defaults to 1 but we recommend setting the mc.cores option to be as many processors as the hardware and RAM allow (up to the number of chains). For non-Windows OS in non-interactive R sessions, forking is used instead of PSOCK clusters.


A named list of parameters to control the sampler's behavior. It defaults to NULL so all the default values are used. The most important control parameters are discussed in the 'Details' section below. For a comprehensive overview see stan.


Character string indicating the estimation approach to use. Can be "sampling" for MCMC (the default), "meanfield" for variational inference with independent normal distributions, or "fullrank" for variational inference with a multivariate normal distribution.


Logical; If TRUE, the future package is used for parallel execution of the chains and argument cores will be ignored. Can be set globally for the current R session via the future option. The execution type is controlled via plan (see the examples section below).


logical; If TRUE (the default), most of the informational messages of compiler and sampler are suppressed. The actual sampling progress is still printed. Set refresh = 0 to turn this off as well. To stop Stan from opening additional progress bars, set open_progress = FALSE.


The seed for random number generation to make results reproducible. If NA (the default), Stan will set the seed randomly.


Either NULL or a character string. In the latter case, the model's Stan code is saved via cat in a text file named after the string supplied in save_model.


A list of further arguments passed to stan_model.


Logical, defaulting to TRUE, indicating whether the dynamic shared object (DSO) compiled from the C++ code for the model will be saved or not. If TRUE, we can draw samples from the same model in another R session using the saved DSO (i.e., without compiling the C++ code again).


Either NULL or a character string. In the latter case, the fitted model object is saved via saveRDS in a file named after the string supplied in file. The .rds extension is added automatically. If the file already exists, brm will load and return the saved model object instead of refitting the model. As existing files won't be overwritten, you have to manually remove the file in order to refit and save the model under an existing file name. The file name is stored in the brmsfit object for later usage.


Further arguments passed to Stan that is to sampling or vb.


Fit a generalized (non-)linear multivariate multilevel model via full Bayesian inference using Stan. A general overview is provided in the vignettes vignette("brms_overview") and vignette("brms_multilevel"). For a full list of available vignettes see vignette(package = "brms").

Formula syntax of brms models

Details of the formula syntax applied in brms can be found in brmsformula.

Families and link functions

Details of families supported by brms can be found in brmsfamily.

Prior distributions

Priors should be specified using the set_prior function. Its documentation contains detailed information on how to correctly specify priors. To find out on which parameters or parameter classes priors can be defined, use get_prior. Default priors are chosen to be non or very weakly informative so that their influence on the results will be negligible and you usually don't have to worry about them. However, after getting more familiar with Bayesian statistics, I recommend you to start thinking about reasonable informative priors for your model parameters: Nearly always, there is at least some prior information available that can be used to improve your inference.

Adjusting the sampling behavior of Stan

In addition to choosing the number of iterations, warmup samples, and chains, users can control the behavior of the NUTS sampler, by using the control argument. The most important reason to use control is to decrease (or eliminate at best) the number of divergent transitions that cause a bias in the obtained posterior samples. Whenever you see the warning "There were x divergent transitions after warmup." you should really think about increasing adapt_delta. To do this, write control = list(adapt_delta = <x>), where <x> should usually be value between 0.8 (current default) and 1. Increasing adapt_delta will slow down the sampler but will decrease the number of divergent transitions threatening the validity of your posterior samples.

Another problem arises when the depth of the tree being evaluated in each iteration is exceeded. This is less common than having divergent transitions, but may also bias the posterior samples. When it happens, Stan will throw out a warning suggesting to increase max_treedepth, which can be accomplished by writing control = list(max_treedepth = <x>) with a positive integer <x> that should usually be larger than the current default of 10. For more details on the control argument see stan.


An object of class brmsfit, which contains the posterior samples along with many other useful information about the model. Use methods(class = "brmsfit") for an overview on available methods.


Paul-Christian Buerkner (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01

Paul-Christian Buerkner (in review). Advanced Bayesian Multilevel Modeling with the R Package brms. arXiv preprint.

See Also

brms, brmsformula, brmsfamily, brmsfit

  • brm
# Poisson regression for the number of seizures in epileptic patients
# using student_t priors for population-level effects
# and half cauchy priors for standard deviations of group-level effects
bprior1 <- prior(student_t(5,0,10), class = b) +
  prior(cauchy(0,2), class = sd)
fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1|patient),
            data = epilepsy, family = poisson(), prior = bprior1)

# generate a summary of the results

# plot the MCMC chains as well as the posterior distributions
plot(fit1, ask = FALSE)

# predict responses based on the fitted model

# plot marginal effects for each predictor
plot(marginal_effects(fit1), ask = FALSE)

# investigate model fit

# Ordinal regression modeling patient's rating of inhaler instructions
# category specific effects are estimated for variable 'treat'
fit2 <- brm(rating ~ period + carry + cs(treat),
            data = inhaler, family = sratio("logit"),
            prior = set_prior("normal(0,5)"), chains = 2)
plot(fit2, ask = FALSE)

# Survival regression modeling the time between the first
# and second recurrence of an infection in kidney patients.
fit3 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
            data = kidney, family = lognormal())
plot(fit3, ask = FALSE)
plot(marginal_effects(fit3), ask = FALSE)

# Probit regression using the binomial family
ntrials <- sample(1:10, 100, TRUE)
success <- rbinom(100, size = ntrials, prob = 0.4)
x <- rnorm(100)
data4 <- data.frame(ntrials, success, x)
fit4 <- brm(success | trials(ntrials) ~ x, data = data4,
            family = binomial("probit"))

# Simple non-linear gaussian model
x <- rnorm(100)
y <- rnorm(100, mean = 2 - 1.5^x, sd = 1)
data5 <- data.frame(x, y)
bprior5 <- prior(normal(0, 2), nlpar = a1) +
  prior(normal(0, 2), nlpar = a2)
fit5 <- brm(bf(y ~ a1 - a2^x, a1 + a2 ~ 1, nl = TRUE),
            data = data5, prior = bprior5)
plot(marginal_effects(fit5), ask = FALSE)

# Normal model with heterogeneous variances
data_het <- data.frame(
  y = c(rnorm(50), rnorm(50, 1, 2)),
  x = factor(rep(c("a", "b"), each = 50))
fit6 <- brm(bf(y ~ x, sigma ~ 0 + x), data = data_het)

# extract estimated residual SDs of both groups
sigmas <- exp(posterior_samples(fit6, "^b_sigma_"))
ggplot(stack(sigmas), aes(values)) +
  geom_density(aes(fill = ind))

# Quantile regression predicting the 25%-quantile
fit7 <- brm(bf(y ~ x, quantile = 0.25), data = data_het,
            family = asym_laplace())

# use the future package for more flexible parallelization
fit7 <- update(fit7, future = TRUE)
# }
# }
Documentation reproduced from package brms, version 2.6.0, License: GPL (>= 3)

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