Package 'brms'

Description

Stan Development Team

The brms package provides an interface to fit Bayesian generalized multivariate (non-)linear multilevel models using Stan, which is a C++ package for obtaining full Bayesian inference (see https://mc-stan.org/). The formula syntax is an extended version of the syntax applied in the lme4 package to provide a familiar and simple interface for performing regression analyses.

Details

The main function of brms is brm, which uses formula syntax to specify a wide range of complex Bayesian models (see brmsformula for details). Based on the supplied formulas, data, and additional information, it writes the Stan code on the fly via stancode, prepares the data via standata and fits the model using Stan.

Subsequently, a large number of post-processing methods can be applied: To get an overview on the estimated parameters, summary or conditional_effects are perfectly suited. Detailed visual analyses can be performed by applying the pp_check and stanplot methods, which both rely on the bayesplot package. Model comparisons can be done via loo and waic, which make use of the loo package as well as via bayes_factor which relies on the bridgesampling package. For a full list of methods to apply, type methods(class = "brmsfit").

Because brms is based on Stan, a C++ compiler is required. The program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On Mac, you should use Xcode. For further instructions on how to get the compilers running, see the prerequisites section at the RStan-Getting-Started page.

When comparing other packages fitting multilevel models to brms, keep in mind that the latter needs to compile models before actually fitting them, which will require between 20 and 40 seconds depending on your machine, operating system and overall model complexity.

Thus, fitting smaller models may be relatively slow as compilation time makes up the majority of the whole running time. For larger / more complex models however, fitting my take several minutes or even hours, so that the compilation time won't make much of a difference for these models.

See vignette("brms_overview") and vignette("brms_multilevel") for a general introduction and overview of brms. For a full list of available vignettes, type vignette(package = "brms").

Author(s)

Maintainer: Paul-Christian Bürkner [email protected]

Other contributors:

• Jonah Gabry [contributor]

• Sebastian Weber [contributor]

• Andrew Johnson [contributor]

• Martin Modrak [contributor]

• Hamada S. Badr [contributor]

• Frank Weber [contributor]

• Aki Vehtari [contributor]

• Mattan S. Ben-Shachar [contributor]

• Hayden Rabel [contributor]

• Simon C. Mills [contributor]

• Stephen Wild [contributor]

• Ven Popov [contributor]

References

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 (2018). Advanced Bayesian Multilevel Modeling with the R Package brms. The R Journal. 10(1), 395–411. doi:10.32614/RJ-2018-017

The Stan Development Team. Stan Modeling Language User's Guide and Reference Manual. https://mc-stan.org/users/documentation/.

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org/

See Also

brm, brmsformula, brmsfamily, brmsfit

Description

Add model fit criteria to model objects

Usage

add_criterion(x, ...)

## S3 method for class 'brmsfit'
add_criterion(
  x,
  criterion,
  model_name = NULL,
  overwrite = FALSE,
  file = NULL,
  force_save = FALSE,
  ...
)

Arguments

Details

Functions add_loo and add_waic are aliases of add_criterion with fixed values for the criterion argument.

Value

An object of the same class as x, but with model fit criteria added for later usage.

Examples

## Not run: 
fit <- brm(count ~ Trt, data = epilepsy)
# add both LOO and WAIC at once
fit <- add_criterion(fit, c("loo", "waic"))
print(fit$criteria$loo)
print(fit$criteria$waic)

## End(Not run)

Description

Deprecated aliases of add_criterion.

Usage

add_loo(x, model_name = NULL, ...)

add_waic(x, model_name = NULL, ...)

add_ic(x, ...)

## S3 method for class 'brmsfit'
add_ic(x, ic = "loo", model_name = NULL, ...)

add_ic(x, ...) <- value

Arguments

Value

An object of the same class as x, but with model fit criteria added for later usage. Previously computed criterion objects will be overwritten.

Description

Compile a stanmodel and add it to a brmsfit object. This enables some advanced functionality of rstan, most notably log_prob and friends, to be used with brms models fitted with other Stan backends.

Usage

add_rstan_model(x, overwrite = FALSE)

Arguments

Value

A (possibly updated) brmsfit object.

Description

Provide additional information on the response variable in brms models, such as censoring, truncation, or known measurement error. Detailed documentation on the use of each of these functions can be found in the Details section of brmsformula (under "Additional response information").

Usage

resp_se(x, sigma = FALSE)

resp_weights(x, scale = FALSE)

resp_trials(x)

resp_thres(x, gr = NA)

resp_cat(x)

resp_dec(x)

resp_bhaz(gr = NA, df = 5, ...)

resp_cens(x, y2 = NA)

resp_trunc(lb = -Inf, ub = Inf)

resp_mi(sdy = NA)

resp_index(x)

resp_rate(denom)

resp_subset(x)

resp_vreal(...)

resp_vint(...)

Arguments

Details

These functions are almost solely useful when called in formulas passed to the brms package. Within formulas, the resp_ prefix may be omitted. More information is given in the 'Details' section of brmsformula (under "Additional response information").

It is highly recommended to use a single data variable as input for x (instead of a more complicated expression) to make sure all post-processing functions work as expected.

Value

A list of additional response information to be processed further by brms.

See Also

brm, brmsformula

Examples

## Not run: 
## Random effects meta-analysis
nstudies <- 20
true_effects <- rnorm(nstudies, 0.5, 0.2)
sei <- runif(nstudies, 0.05, 0.3)
outcomes <- rnorm(nstudies, true_effects, sei)
data1 <- data.frame(outcomes, sei)
fit1 <- brm(outcomes | se(sei, sigma = TRUE) ~ 1,
            data = data1)
summary(fit1)

## Probit regression using the binomial family
n <- sample(1:10, 100, TRUE)  # number of trials
success <- rbinom(100, size = n, prob = 0.4)
x <- rnorm(100)
data2 <- data.frame(n, success, x)
fit2 <- brm(success | trials(n) ~ x, data = data2,
            family = binomial("probit"))
summary(fit2)

## 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())
summary(fit3)

## Poisson model with truncated counts
fit4 <- brm(count | trunc(ub = 104) ~ zBase * Trt,
            data = epilepsy, family = poisson())
summary(fit4)

## End(Not run)

Description

Set up an autoregressive (AR) term of order p in brms. The function does not evaluate its arguments – it exists purely to help set up a model with AR terms.

Usage

ar(time = NA, gr = NA, p = 1, cov = FALSE)

Arguments

Value

An object of class 'arma_term', which is a list of arguments to be interpreted by the formula parsing functions of brms.

See Also

autocor-terms, arma, ma

Examples

## Not run: 
data("LakeHuron")
LakeHuron <- as.data.frame(LakeHuron)
fit <- brm(x ~ ar(p = 2), data = LakeHuron)
summary(fit)

## End(Not run)

Description

Set up an autoregressive moving average (ARMA) term of order (p, q) in brms. The function does not evaluate its arguments – it exists purely to help set up a model with ARMA terms.

Usage

arma(time = NA, gr = NA, p = 1, q = 1, cov = FALSE)

Arguments

Value

An object of class 'arma_term', which is a list of arguments to be interpreted by the formula parsing functions of brms.

See Also

autocor-terms, ar, ma,

Examples

## Not run: 
data("LakeHuron")
LakeHuron <- as.data.frame(LakeHuron)
fit <- brm(x ~ arma(p = 2, q = 1), data = LakeHuron)
summary(fit)

## End(Not run)

Description

Try to transform an object into a brmsprior object.

Usage

as.brmsprior(x)

Arguments

Value

A brmsprior object if the transformation was possible.

Description

Extract posterior draws in conventional formats as data.frames, matrices, or arrays.

Usage

## S3 method for class 'brmsfit'
as.data.frame(
  x,
  row.names = NULL,
  optional = TRUE,
  pars = NA,
  variable = NULL,
  draw = NULL,
  subset = NULL,
  ...
)

## S3 method for class 'brmsfit'
as.matrix(x, pars = NA, variable = NULL, draw = NULL, subset = NULL, ...)

## S3 method for class 'brmsfit'
as.array(x, pars = NA, variable = NULL, draw = NULL, subset = NULL, ...)

Arguments

Value

A data.frame, matrix, or array containing the posterior draws.

See Also

as_draws, subset_draws

Description

The as.mcmc method is deprecated. We recommend using the more modern and consistent as_draws_* extractor functions of the posterior package instead.

Usage

## S3 method for class 'brmsfit'
as.mcmc(
  x,
  pars = NA,
  fixed = FALSE,
  combine_chains = FALSE,
  inc_warmup = FALSE,
  ...
)

Arguments

Value

If combine_chains = TRUE an mcmc object is returned. If combine_chains = FALSE an mcmc.list object is returned.

Description

Density, distribution function, quantile function and random generation for the asymmetric Laplace distribution with location mu, scale sigma and asymmetry parameter quantile.

Usage

dasym_laplace(x, mu = 0, sigma = 1, quantile = 0.5, log = FALSE)

pasym_laplace(
  q,
  mu = 0,
  sigma = 1,
  quantile = 0.5,
  lower.tail = TRUE,
  log.p = FALSE
)

qasym_laplace(
  p,
  mu = 0,
  sigma = 1,
  quantile = 0.5,
  lower.tail = TRUE,
  log.p = FALSE
)

rasym_laplace(n, mu = 0, sigma = 1, quantile = 0.5)

Arguments

Details

See vignette("brms_families") for details on the parameterization.

Description

Specify autocorrelation terms in brms models. Currently supported terms are arma, ar, ma, cosy, unstr, sar, car, and fcor. Terms can be directly specified within the formula, or passed to the autocor argument of brmsformula in the form of a one-sided formula. For deprecated ways of specifying autocorrelation terms, see cor_brms.

Details

The autocor term functions are almost solely useful when called in formulas passed to the brms package. They do not evaluate its arguments – but exist purely to help set up a model with autocorrelation terms.

See Also

brmsformula, acformula, arma, ar, ma, cosy, unstr, sar, car, fcor

Examples

# specify autocor terms within the formula
y ~ x + arma(p = 1, q = 1) + car(M)

# specify autocor terms in the 'autocor' argument
bf(y ~ x, autocor = ~ arma(p = 1, q = 1) + car(M))

# specify autocor terms via 'acformula'
bf(y ~ x) + acformula(~ arma(p = 1, q = 1) + car(M))

Description

(Deprecated) Extract Autocorrelation Objects

Usage

## S3 method for class 'brmsfit'
autocor(object, resp = NULL, ...)

autocor(object, ...)

Arguments

Value

A cor_brms object or a list of such objects for multivariate models. Not supported for models fitted with brms 2.11.1 or higher.

Description

Compute Bayes factors from marginal likelihoods.

Usage

## S3 method for class 'brmsfit'
bayes_factor(x1, x2, log = FALSE, ...)

Arguments

Details

Computing the marginal likelihood requires samples of all variables defined in Stan's parameters block to be saved. Otherwise bayes_factor cannot be computed. Thus, please set save_all_pars = TRUE in the call to brm, if you are planning to apply bayes_factor to your models.

The computation of Bayes factors based on bridge sampling requires a lot more posterior samples than usual. A good conservative rule of thumb is perhaps 10-fold more samples (read: the default of 4000 samples may not be enough in many cases). If not enough posterior samples are provided, the bridge sampling algorithm tends to be unstable, leading to considerably different results each time it is run. We thus recommend running bayes_factor multiple times to check the stability of the results.

More details are provided under bridgesampling::bayes_factor.

See Also

bridge_sampler, post_prob

Examples

## Not run: 
# model with the treatment effect
fit1 <- brm(
  count ~ zAge + zBase + Trt,
  data = epilepsy, family = negbinomial(),
  prior = prior(normal(0, 1), class = b),
  save_all_pars = TRUE
)
summary(fit1)

# model without the treatment effect
fit2 <- brm(
  count ~ zAge + zBase,
  data = epilepsy, family = negbinomial(),
  prior = prior(normal(0, 1), class = b),
  save_all_pars = TRUE
)
summary(fit2)

# compute the bayes factor
bayes_factor(fit1, fit2)

## End(Not run)

Description

Compute a Bayesian version of R-squared for regression models

Usage

## S3 method for class 'brmsfit'
bayes_R2(
  object,
  resp = NULL,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

Arguments

Details

For an introduction to the approach, see Gelman et al. (2018) and https://github.com/jgabry/bayes_R2/.

Value

If summary = TRUE, an M x C matrix is returned (M = number of response variables and c = length(probs) + 2) containing summary statistics of the Bayesian R-squared values. If summary = FALSE, the posterior draws of the Bayesian R-squared values are returned in an S x M matrix (S is the number of draws).

References

Andrew Gelman, Ben Goodrich, Jonah Gabry & Aki Vehtari. (2018). R-squared for Bayesian regression models, The American Statistician. 10.1080/00031305.2018.1549100 (Preprint available at https://stat.columbia.edu/~gelman/research/published/bayes_R2_v3.pdf)

Examples

## Not run: 
fit <- brm(mpg ~ wt + cyl, data = mtcars)
summary(fit)
bayes_R2(fit)

# compute R2 with new data
nd <- data.frame(mpg = c(10, 20, 30), wt = c(4, 3, 2), cyl = c(8, 6, 4))
bayes_R2(fit, newdata = nd)

## End(Not run)

Description

Cumulative density & mass functions, and random number generation for the Beta-binomial distribution using the following re-parameterisation of the Stan Beta-binomial definition:

• mu = alpha * beta mean probability of trial success.

• phi = (1 - mu) * beta precision or over-dispersion, component.

Usage

dbeta_binomial(x, size, mu, phi, log = FALSE)

pbeta_binomial(q, size, mu, phi, lower.tail = TRUE, log.p = FALSE)

rbeta_binomial(n, size, mu, phi)

Arguments

Description

Computes log marginal likelihood via bridge sampling, which can be used in the computation of bayes factors and posterior model probabilities. The brmsfit method is just a thin wrapper around the corresponding method for stanfit objects.

Usage

## S3 method for class 'brmsfit'
bridge_sampler(samples, recompile = FALSE, ...)

Arguments

Details

Computing the marginal likelihood requires samples of all variables defined in Stan's parameters block to be saved. Otherwise bridge_sampler cannot be computed. Thus, please set save_pars = save_pars(all = TRUE) in the call to brm, if you are planning to apply bridge_sampler to your models.

The computation of marginal likelihoods based on bridge sampling requires a lot more posterior draws than usual. A good conservative rule of thump is perhaps 10-fold more draws (read: the default of 4000 draws may not be enough in many cases). If not enough posterior draws are provided, the bridge sampling algorithm tends to be unstable leading to considerably different results each time it is run. We thus recommend running bridge_sampler multiple times to check the stability of the results.

More details are provided under bridgesampling::bridge_sampler.

See Also

bayes_factor, post_prob

Examples

## Not run: 
# model with the treatment effect
fit1 <- brm(
  count ~ zAge + zBase + Trt,
  data = epilepsy, family = negbinomial(),
  prior = prior(normal(0, 1), class = b),
  save_pars = save_pars(all = TRUE)
)
summary(fit1)
bridge_sampler(fit1)

# model without the treatment effect
fit2 <- brm(
  count ~ zAge + zBase,
  data = epilepsy, family = negbinomial(),
  prior = prior(normal(0, 1), class = b),
  save_pars = save_pars(all = TRUE)
)
summary(fit2)
bridge_sampler(fit2)

## End(Not run)

Description

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.

Usage

brm(
  formula,
  data,
  family = gaussian(),
  prior = NULL,
  autocor = NULL,
  data2 = NULL,
  cov_ranef = NULL,
  sample_prior = "no",
  sparse = NULL,
  knots = NULL,
  drop_unused_levels = TRUE,
  stanvars = NULL,
  stan_funs = NULL,
  fit = NA,
  save_pars = getOption("brms.save_pars", NULL),
  save_ranef = NULL,
  save_mevars = NULL,
  save_all_pars = NULL,
  init = NULL,
  inits = NULL,
  chains = 4,
  iter = 2000,
  warmup = floor(iter/2),
  thin = 1,
  cores = getOption("mc.cores", 1),
  threads = getOption("brms.threads", NULL),
  opencl = getOption("brms.opencl", NULL),
  normalize = getOption("brms.normalize", TRUE),
  control = NULL,
  algorithm = getOption("brms.algorithm", "sampling"),
  backend = getOption("brms.backend", "rstan"),
  future = getOption("future", FALSE),
  silent = 1,
  seed = NA,
  save_model = NULL,
  stan_model_args = list(),
  file = NULL,
  file_compress = TRUE,
  file_refit = getOption("brms.file_refit", "never"),
  empty = FALSE,
  rename = TRUE,
  ...
)

Arguments

Details

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 default_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 draws, 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 draws. 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 draws.

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 draws. 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.

Value

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

Author(s)

Paul-Christian Buerkner [email protected]

References

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 (2018). Advanced Bayesian Multilevel Modeling with the R Package brms. The R Journal. 10(1), 395–411. doi:10.32614/RJ-2018-017

See Also

brms, brmsformula, brmsfamily, brmsfit

Examples

## Not run: 
# Poisson regression for the number of seizures in epileptic patients
fit1 <- brm(
  count ~ zBase * Trt + (1|patient),
  data = epilepsy, family = poisson(),
  prior = prior(normal(0, 10), class = b) +
    prior(cauchy(0, 2), class = sd)
)

# generate a summary of the results
summary(fit1)

# plot the MCMC chains as well as the posterior distributions
plot(fit1)

# predict responses based on the fitted model
head(predict(fit1))

# plot conditional effects for each predictor
plot(conditional_effects(fit1), ask = FALSE)

# investigate model fit
loo(fit1)
pp_check(fit1)


# 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)
summary(fit2)
plot(fit2, ask = FALSE)
WAIC(fit2)


# 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())
summary(fit3)
plot(fit3, ask = FALSE)
plot(conditional_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"))
summary(fit4)


# Non-linear Gaussian model
fit5 <- brm(
  bf(cum ~ ult * (1 - exp(-(dev/theta)^omega)),
     ult ~ 1 + (1|AY), omega ~ 1, theta ~ 1,
     nl = TRUE),
  data = loss, family = gaussian(),
  prior = c(
    prior(normal(5000, 1000), nlpar = "ult"),
    prior(normal(1, 2), nlpar = "omega"),
    prior(normal(45, 10), nlpar = "theta")
  ),
  control = list(adapt_delta = 0.9)
)
summary(fit5)
conditional_effects(fit5)


# 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)
summary(fit6)
plot(fit6)
conditional_effects(fit6)

# extract estimated residual SDs of both groups
sigmas <- exp(as.data.frame(fit6, variable = "^b_sigma_", regex = TRUE))
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())
summary(fit7)
conditional_effects(fit7)


# use the future package for more flexible parallelization
library(future)
plan(multisession, workers = 4)
fit7 <- update(fit7, future = TRUE)


# fit a model manually via rstan
scode <- stancode(count ~ Trt, data = epilepsy)
sdata <- standata(count ~ Trt, data = epilepsy)
stanfit <- rstan::stan(model_code = scode, data = sdata)
# feed the Stan model back into brms
fit8 <- brm(count ~ Trt, data = epilepsy, empty = TRUE)
fit8$fit <- stanfit
fit8 <- rename_pars(fit8)
summary(fit8)

## End(Not run)

Description

Run the same brms model on multiple datasets and then combine the results into one fitted model object. This is useful in particular for multiple missing value imputation, where the same model is fitted on multiple imputed data sets. Models can be run in parallel using the future package.

Usage

brm_multiple(
  formula,
  data,
  family = gaussian(),
  prior = NULL,
  data2 = NULL,
  autocor = NULL,
  cov_ranef = NULL,
  sample_prior = c("no", "yes", "only"),
  sparse = NULL,
  knots = NULL,
  stanvars = NULL,
  stan_funs = NULL,
  silent = 1,
  recompile = FALSE,
  combine = TRUE,
  fit = NA,
  algorithm = getOption("brms.algorithm", "sampling"),
  seed = NA,
  file = NULL,
  file_compress = TRUE,
  file_refit = getOption("brms.file_refit", "never"),
  ...
)

Arguments

Details

The combined model may issue false positive convergence warnings, as the MCMC chains corresponding to different datasets may not necessarily overlap, even if each of the original models did converge. To find out whether each of the original models converged, subset the draws belonging to the individual models and then run convergence diagnostics. See Examples below for details.

Value

If combine = TRUE a brmsfit_multiple object, which inherits from class brmsfit and behaves essentially the same. If combine = FALSE a list of brmsfit objects.

Examples

## Not run: 
library(mice)
m <- 5
imp <- mice(nhanes2, m = m)

# fit the model using mice and lm
fit_imp1 <- with(lm(bmi ~ age + hyp + chl), data = imp)
summary(pool(fit_imp1))

# fit the model using brms
fit_imp2 <- brm_multiple(bmi ~ age + hyp + chl, data = imp, chains = 1)
summary(fit_imp2)
plot(fit_imp2, variable = "^b_", regex = TRUE)

# investigate convergence of the original models
library(posterior)
draws <- as_draws_array(fit_imp2)
# every dataset has just one chain here
draws_per_dat <- lapply(1:m, \(i) subset_draws(draws, chain = i))
lapply(draws_per_dat, summarise_draws, default_convergence_measures())

# use the future package for parallelization
library(future)
plan(multisession, workers = 4)
fit_imp3 <- brm_multiple(bmi ~ age + hyp + chl, data = imp, chains = 1)
summary(fit_imp3)

## End(Not run)

Description

Family objects provide a convenient way to specify the details of the models used by many model fitting functions. The family functions presented here are for use with brms only and will **not** work with other model fitting functions such as glm or glmer. However, the standard family functions as described in family will work with brms. You can also specify custom families for use in brms with the custom_family function.

Usage

brmsfamily(
  family,
  link = NULL,
  link_sigma = "log",
  link_shape = "log",
  link_nu = "logm1",
  link_phi = "log",
  link_kappa = "log",
  link_beta = "log",
  link_zi = "logit",
  link_hu = "logit",
  link_zoi = "logit",
  link_coi = "logit",
  link_disc = "log",
  link_bs = "log",
  link_ndt = "log",
  link_bias = "logit",
  link_xi = "log1p",
  link_alpha = "identity",
  link_quantile = "logit",
  threshold = "flexible",
  refcat = NULL
)

student(link = "identity", link_sigma = "log", link_nu = "logm1")

bernoulli(link = "logit")

beta_binomial(link = "logit", link_phi = "log")

negbinomial(link = "log", link_shape = "log")

geometric(link = "log")

lognormal(link = "identity", link_sigma = "log")

shifted_lognormal(link = "identity", link_sigma = "log", link_ndt = "log")

skew_normal(link = "identity", link_sigma = "log", link_alpha = "identity")

exponential(link = "log")

weibull(link = "log", link_shape = "log")

frechet(link = "log", link_nu = "logm1")

gen_extreme_value(link = "identity", link_sigma = "log", link_xi = "log1p")

exgaussian(link = "identity", link_sigma = "log", link_beta = "log")

wiener(
  link = "identity",
  link_bs = "log",
  link_ndt = "log",
  link_bias = "logit"
)

Beta(link = "logit", link_phi = "log")

dirichlet(link = "logit", link_phi = "log", refcat = NULL)

logistic_normal(link = "identity", link_sigma = "log", refcat = NULL)

von_mises(link = "tan_half", link_kappa = "log")

asym_laplace(link = "identity", link_sigma = "log", link_quantile = "logit")

cox(link = "log")

hurdle_poisson(link = "log", link_hu = "logit")

hurdle_negbinomial(link = "log", link_shape = "log", link_hu = "logit")

hurdle_gamma(link = "log", link_shape = "log", link_hu = "logit")

hurdle_lognormal(link = "identity", link_sigma = "log", link_hu = "logit")

hurdle_cumulative(
  link = "logit",
  link_hu = "logit",
  link_disc = "log",
  threshold = "flexible"
)

zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit")

zero_one_inflated_beta(
  link = "logit",
  link_phi = "log",
  link_zoi = "logit",
  link_coi = "logit"
)

zero_inflated_poisson(link = "log", link_zi = "logit")

zero_inflated_negbinomial(link = "log", link_shape = "log", link_zi = "logit")

zero_inflated_binomial(link = "logit", link_zi = "logit")

zero_inflated_beta_binomial(
  link = "logit",
  link_phi = "log",
  link_zi = "logit"
)

categorical(link = "logit", refcat = NULL)

multinomial(link = "logit", refcat = NULL)

cumulative(link = "logit", link_disc = "log", threshold = "flexible")

sratio(link = "logit", link_disc = "log", threshold = "flexible")

cratio(link = "logit", link_disc = "log", threshold = "flexible")

acat(link = "logit", link_disc = "log", threshold = "flexible")

Arguments

Details

Below, we list common use cases for the different families. This list is not ment to be exhaustive.

• Family gaussian can be used for linear regression.

• Family student can be used for robust linear regression that is less influenced by outliers.

• Family skew_normal can handle skewed responses in linear regression.

• Families poisson, negbinomial, and geometric can be used for regression of unbounded count data.

• Families bernoulli, binomial, and beta_binomial can be used for binary regression (i.e., most commonly logistic regression).

• Families categorical and multinomial can be used for multi-logistic regression when there are more than two possible outcomes.

• Families cumulative, cratio ('continuation ratio'), sratio ('stopping ratio'), and acat ('adjacent category') leads to ordinal regression.

• Families Gamma, weibull, exponential, lognormal, frechet, inverse.gaussian, and cox (Cox proportional hazards model) can be used (among others) for time-to-event regression also known as survival regression.

• Families weibull, frechet, and gen_extreme_value ('generalized extreme value') allow for modeling extremes.

• Families beta, dirichlet, and logistic_normal can be used to model responses representing rates or probabilities.

• Family asym_laplace allows for quantile regression when fixing the auxiliary quantile parameter to the quantile of interest.

• Family exgaussian ('exponentially modified Gaussian') and shifted_lognormal are especially suited to model reaction times.

• Family wiener provides an implementation of the Wiener diffusion model. For this family, the main formula predicts the drift parameter 'delta' and all other parameters are modeled as auxiliary parameters (see brmsformula for details).

• Families hurdle_poisson, hurdle_negbinomial, hurdle_gamma, hurdle_lognormal, zero_inflated_poisson, zero_inflated_negbinomial, zero_inflated_binomial, zero_inflated_beta_binomial, zero_inflated_beta, zero_one_inflated_beta, and hurdle_cumulative allow to estimate zero-inflated and hurdle models. These models can be very helpful when there are many zeros in the data (or ones in case of one-inflated models) that cannot be explained by the primary distribution of the response.

Below, we list all possible links for each family. The first link mentioned for each family is the default.

• Families gaussian, student, skew_normal, exgaussian, asym_laplace, and gen_extreme_value support the links (as names) identity, log, inverse, and softplus.

• Families poisson, negbinomial, geometric, zero_inflated_poisson, zero_inflated_negbinomial, hurdle_poisson, and hurdle_negbinomial support log, identity, sqrt, and softplus.

• Families binomial, bernoulli, beta_binomial, zero_inflated_binomial, zero_inflated_beta_binomial, Beta, zero_inflated_beta, and zero_one_inflated_beta support logit, probit, probit_approx, cloglog, cauchit, identity, and log.

• Families cumulative, cratio, sratio, acat, and hurdle_cumulative support logit, probit, probit_approx, cloglog, and cauchit.

• Families categorical, multinomial, and dirichlet support logit.

• Families Gamma, weibull, exponential, frechet, and hurdle_gamma support log, identity, inverse, and softplus.

• Families lognormal and hurdle_lognormal support identity and inverse.

• Family logistic_normal supports identity.

• Family inverse.gaussian supports 1/mu^2, inverse, identity, log, and softplus.

• Family von_mises supports tan_half and identity.

• Family cox supports log, identity, and softplus for the proportional hazards parameter.

• Family wiener supports identity, log, and softplus for the main parameter which represents the drift rate.

Please note that when calling the Gamma family function of the stats package, the default link will be inverse instead of log although the latter is the default in brms. Also, when using the family functions gaussian, binomial, poisson, and Gamma of the stats package (see family), special link functions such as softplus or cauchit won't work. In this case, you have to use brmsfamily to specify the family with corresponding link function.

See Also

brm, family, customfamily

Examples

# create a family object
 (fam1 <- student("log"))
 # alternatively use the brmsfamily function
 (fam2 <- brmsfamily("student", "log"))
 # both leads to the same object
 identical(fam1, fam2)

Description

Models fitted with the brms package are represented as a brmsfit object, which contains the posterior draws (samples), model formula, Stan code, relevant data, and other information.

Details

See methods(class = "brmsfit") for an overview of available methods.

Slots

formula

A brmsformula object.

data

A data.frame containing all variables used in the model.

data2

A list of data objects which cannot be passed via data.

prior

A brmsprior object containing information on the priors used in the model.

stanvars

A stanvars object.

model

The model code in Stan language.

exclude

The names of the parameters for which draws are not saved.

algorithm

The name of the algorithm used to fit the model.

backend

The name of the backend used to fit the model.

threads

An object of class 'brmsthreads' created by threading.

opencl

An object of class 'brmsopencl' created by opencl.

stan_args

Named list of additional control arguments that were passed to the Stan backend directly.

fit

An object of class stanfit among others containing the posterior draws.

basis

An object that contains a small subset of the Stan data created at fitting time, which is needed to process new data correctly.

criteria

An empty list for adding model fit criteria after estimation of the model.

file

Optional name of a file in which the model object was stored in or loaded from.

version

The versions of brms and rstan with which the model was fitted.

family

(Deprecated) A brmsfamily object.

autocor

(Deprecated) An cor_brms object containing the autocorrelation structure if specified.

ranef

(Deprecated) A data.frame containing the group-level structure.

cov_ranef

(Deprecated) A list of customized group-level covariance matrices.

stan_funs

(Deprecated) A character string of length one or NULL.

data.name

(Deprecated) The name of data as specified by the user.

See Also

brms, brm, brmsformula, brmsfamily

Description

Set up a model formula for use in the brms package allowing to define (potentially non-linear) additive multilevel models for all parameters of the assumed response distribution.

Usage

brmsformula(
  formula,
  ...,
  flist = NULL,
  family = NULL,
  autocor = NULL,
  nl = NULL,
  loop = NULL,
  center = NULL,
  cmc = NULL,
  sparse = NULL,
  decomp = NULL,
  unused = NULL
)

Arguments

Details

General formula structure

The formula argument accepts formulas of the following syntax:

response | aterms ~ pterms + (gterms | group)

The pterms part contains effects that are assumed to be the same across observations. We call them 'population-level' or 'overall' effects, or (adopting frequentist vocabulary) 'fixed' effects. The optional gterms part may contain effects that are assumed to vary across grouping variables specified in group. We call them 'group-level' or 'varying' effects, or (adopting frequentist vocabulary) 'random' effects, although the latter name is misleading in a Bayesian context. For more details type vignette("brms_overview") and vignette("brms_multilevel").

Group-level terms

Multiple grouping factors each with multiple group-level effects are possible. (Of course we can also run models without any group-level effects.) Instead of | you may use || in grouping terms to prevent correlations from being modeled. Equivalently, the cor argument of the gr function can be used for this purpose, for example, (1 + x || g) is equivalent to (1 + x | gr(g, cor = FALSE)).

It is also possible to model different group-level terms of the same grouping factor as correlated (even across different formulas, e.g., in non-linear models) by using |<ID>| instead of |. All group-level terms sharing the same ID will be modeled as correlated. If, for instance, one specifies the terms (1+x|i|g) and (1+z|i|g) somewhere in the formulas passed to brmsformula, correlations between the corresponding group-level effects will be estimated. In the above example, i is not a variable in the data but just a symbol to indicate correlations between multiple group-level terms. Equivalently, the id argument of the gr function can be used as well, for example, (1 + x | gr(g, id = "i")).

If levels of the grouping factor belong to different sub-populations, it may be reasonable to assume a different covariance matrix for each of the sub-populations. For instance, the variation within the treatment group and within the control group in a randomized control trial might differ. Suppose that y is the outcome, and x is the factor indicating the treatment and control group. Then, we could estimate different hyper-parameters of the varying effects (in this case a varying intercept) for treatment and control group via y ~ x + (1 | gr(subject, by = x)).

You can specify multi-membership terms using the mm function. For instance, a multi-membership term with two members could be (1 | mm(g1, g2)), where g1 and g2 specify the first and second member, respectively. Moreover, if a covariate x varies across the levels of the grouping-factors g1 and g2, we can save the respective covariate values in the variables x1 and x2 and then model the varying effect as (1 + mmc(x1, x2) | mm(g1, g2)).

Special predictor terms

Flexible non-linear smooth terms can modeled using the s and t2 functions in the pterms part of the model formula. This allows to fit generalized additive mixed models (GAMMs) with brms. The implementation is similar to that used in the gamm4 package. For more details on this model class see gam and gamm.

Gaussian process terms can be fitted using the gp function in the pterms part of the model formula. Similar to smooth terms, Gaussian processes can be used to model complex non-linear relationships, for instance temporal or spatial autocorrelation. However, they are computationally demanding and are thus not recommended for very large datasets or approximations need to be used.

The pterms and gterms parts may contain four non-standard effect types namely monotonic, measurement error, missing value, and category specific effects, which can be specified using terms of the form mo(predictor), me(predictor, sd_predictor), mi(predictor), and cs(<predictors>), respectively. Category specific effects can only be estimated in ordinal models and are explained in more detail in the package's main vignette (type vignette("brms_overview")). The other three effect types are explained in the following.

A monotonic predictor must either be integer valued or an ordered factor, which is the first difference to an ordinary continuous predictor. More importantly, predictor categories (or integers) are not assumed to be equidistant with respect to their effect on the response variable. Instead, the distance between adjacent predictor categories (or integers) is estimated from the data and may vary across categories. This is realized by parameterizing as follows: One parameter takes care of the direction and size of the effect similar to an ordinary regression parameter, while an additional parameter vector estimates the normalized distances between consecutive predictor categories. A main application of monotonic effects are ordinal predictors that can this way be modeled without (falsely) treating them as continuous or as unordered categorical predictors. For more details and examples see vignette("brms_monotonic").

Quite often, predictors are measured and as such naturally contain measurement error. Although most researchers are well aware of this problem, measurement error in predictors is ignored in most regression analyses, possibly because only few packages allow for modeling it. Notably, measurement error can be handled in structural equation models, but many more general regression models (such as those featured by brms) cannot be transferred to the SEM framework. In brms, effects of noise-free predictors can be modeled using the me (for 'measurement error') function. If, say, y is the response variable and x is a measured predictor with known measurement error sdx, we can simply include it on the right-hand side of the model formula via y ~ me(x, sdx). This can easily be extended to more general formulas. If x2 is another measured predictor with corresponding error sdx2 and z is a predictor without error (e.g., an experimental setting), we can model all main effects and interactions of the three predictors in the well known manner: y ~ me(x, sdx) * me(x2, sdx2) * z. The me function is soft deprecated in favor of the more flexible and consistent mi function (see below).

When a variable contains missing values, the corresponding rows will be excluded from the data by default (row-wise exclusion). However, quite often we want to keep these rows and instead estimate the missing values. There are two approaches for this: (a) Impute missing values before the model fitting for instance via multiple imputation (see brm_multiple for a way to handle multiple imputed datasets). (b) Impute missing values on the fly during model fitting. The latter approach is explained in the following. Using a variable with missing values as predictors requires two things, First, we need to specify that the predictor contains missings that should to be imputed. If, say, y is the primary response, x is a predictor with missings and z is a predictor without missings, we go for y ~ mi(x) + z. Second, we need to model x as an additional response with corresponding predictors and the addition term mi(). In our example, we could write x | mi() ~ z. Measurement error may be included via the sdy argument, say, x | mi(sdy = se) ~ z. See mi for examples with real data.

Autocorrelation terms

Autocorrelation terms can be directly specified inside the pterms part as well. Details can be found in autocor-terms.

Additional response information

Another special of the brms formula syntax is the optional aterms part, which may contain multiple terms of the form fun(<variable>) separated by + each providing special information on the response variable. fun can be replaced with either se, weights, subset, cens, trunc, trials, cat, dec, rate, vreal, or vint. Their meanings are explained below (see also addition-terms).

For families gaussian, student and skew_normal, it is possible to specify standard errors of the observations, thus allowing to perform meta-analysis. Suppose that the variable yi contains the effect sizes from the studies and sei the corresponding standard errors. Then, fixed and random effects meta-analyses can be conducted using the formulas yi | se(sei) ~ 1 and yi | se(sei) ~ 1 + (1|study), respectively, where study is a variable uniquely identifying every study. If desired, meta-regression can be performed via yi | se(sei) ~ 1 + mod1 + mod2 + (1|study) or
yi | se(sei) ~ 1 + mod1 + mod2 + (1 + mod1 + mod2|study), where mod1 and mod2 represent moderator variables. By default, the standard errors replace the parameter sigma. To model sigma in addition to the known standard errors, set argument sigma in function se to TRUE, for instance, yi | se(sei, sigma = TRUE) ~ 1.

For all families, weighted regression may be performed using weights in the aterms part. Internally, this is implemented by multiplying the log-posterior values of each observation by their corresponding weights. Suppose that variable wei contains the weights and that yi is the response variable. Then, formula yi | weights(wei) ~ predictors implements a weighted regression.

For multivariate models, subset may be used in the aterms part, to use different subsets of the data in different univariate models. For instance, if sub is a logical variable and y is the response of one of the univariate models, we may write y | subset(sub) ~ predictors so that y is predicted only for those observations for which sub evaluates to TRUE.

For log-linear models such as poisson models, rate may be used in the aterms part to specify the denominator of a response that is expressed as a rate. The numerator is given by the actual response variable and has a distribution according to the family as usual. Using rate(denom) is equivalent to adding offset(log(denom)) to the linear predictor of the main parameter but the former is arguably more convenient and explicit.

With the exception of categorical and ordinal families, left, right, and interval censoring can be modeled through y | cens(censored) ~ predictors. The censoring variable (named censored in this example) should contain the values 'left', 'none', 'right', and 'interval' (or equivalently -1, 0, 1, and 2) to indicate that the corresponding observation is left censored, not censored, right censored, or interval censored. For interval censored data, a second variable (let's call it y2) has to be passed to cens. In this case, the formula has the structure y | cens(censored, y2) ~ predictors. While the lower bounds are given in y, the upper bounds are given in y2 for interval censored data. Intervals are assumed to be open on the left and closed on the right: (y, y2].

With the exception of categorical and ordinal families, the response distribution can be truncated using the trunc function in the addition part. If the response variable is truncated between, say, 0 and 100, we can specify this via yi | trunc(lb = 0, ub = 100) ~ predictors. Instead of numbers, variables in the data set can also be passed allowing for varying truncation points across observations. Defining only one of the two arguments in trunc leads to one-sided truncation.

For all continuous families, missing values in the responses can be imputed within Stan by using the addition term mi. This is mostly useful in combination with mi predictor terms as explained above under 'Special predictor terms'.

For families binomial and zero_inflated_binomial, addition should contain a variable indicating the number of trials underlying each observation. In lme4 syntax, we may write for instance cbind(success, n - success), which is equivalent to success | trials(n) in brms syntax. If the number of trials is constant across all observations, say 10, we may also write success | trials(10). Please note that the cbind() syntax will not work in brms in the expected way because this syntax is reserved for other purposes.

For all ordinal families, aterms may contain a term thres(number) to specify the number thresholds (e.g, thres(6)), which should be equal to the total number of response categories - 1. If not given, the number of thresholds is calculated from the data. If different threshold vectors should be used for different subsets of the data, the gr argument can be used to provide the grouping variable (e.g, thres(6, gr = item), if item is the grouping variable). In this case, the number of thresholds can also be a variable in the data with different values per group.

A deprecated quasi alias of thres() is cat() with which the total number of response categories (i.e., number of thresholds + 1) can be specified.

In Wiener diffusion models (family wiener) the addition term dec is mandatory to specify the (vector of) binary decisions corresponding to the reaction times. Non-zero values will be treated as a response on the upper boundary of the diffusion process and zeros will be treated as a response on the lower boundary. Alternatively, the variable passed to dec might also be a character vector consisting of 'lower' and 'upper'.

All families support the index addition term to uniquely identify each observation of the corresponding response variable. Currently, index is primarily useful in combination with the subset addition and mi terms.

For custom families, it is possible to pass an arbitrary number of real and integer vectors via the addition terms vreal and vint, respectively. An example is provided in vignette('brms_customfamilies'). To pass multiple vectors of the same data type, provide them separated by commas inside a single vreal or vint statement.

Multiple addition terms of different types may be specified at the same time using the + operator. For example, the formula formula = yi | se(sei) + cens(censored) ~ 1 implies a censored meta-analytic model.

The addition argument disp (short for dispersion) has been removed in version 2.0. You may instead use the distributional regression approach by specifying sigma ~ 1 + offset(log(xdisp)) or shape ~ 1 + offset(log(xdisp)), where xdisp is the variable being previously passed to disp.

Parameterization of the population-level intercept

By default, the population-level intercept (if incorporated) is estimated separately and not as part of population-level parameter vector b As a result, priors on the intercept also have to be specified separately. Furthermore, to increase sampling efficiency, the population-level design matrix X is centered around its column means X_means if the intercept is incorporated. This leads to a temporary bias in the intercept equal to <X_means, b>, where <,> is the scalar product. The bias is corrected after fitting the model, but be aware that you are effectively defining a prior on the intercept of the centered design matrix not on the real intercept. You can turn off this special handling of the intercept by setting argument center to FALSE. For more details on setting priors on population-level intercepts, see set_prior.

This behavior can be avoided by using the reserved (and internally generated) variable Intercept. Instead of y ~ x, you may write y ~ 0 + Intercept + x. This way, priors can be defined on the real intercept, directly. In addition, the intercept is just treated as an ordinary population-level effect and thus priors defined on b will also apply to it. Note that this parameterization may be less efficient than the default parameterization discussed above.

Formula syntax for non-linear models

In brms, it is possible to specify non-linear models of arbitrary complexity. The non-linear model can just be specified within the formula argument. Suppose, that we want to predict the response y through the predictor x, where x is linked to y through y = alpha - beta * lambda^x, with parameters alpha, beta, and lambda. This is certainly a non-linear model being defined via formula = y ~ alpha - beta * lambda^x (addition arguments can be added in the same way as for ordinary formulas). To tell brms that this is a non-linear model, we set argument nl to TRUE. Now we have to specify a model for each of the non-linear parameters. Let's say we just want to estimate those three parameters with no further covariates or random effects. Then we can pass alpha + beta + lambda ~ 1 or equivalently (and more flexible) alpha ~ 1, beta ~ 1, lambda ~ 1 to the ... argument. This can, of course, be extended. If we have another predictor z and observations nested within the grouping factor g, we may write for instance alpha ~ 1, beta ~ 1 + z + (1|g), lambda ~ 1. The formula syntax described above applies here as well. In this example, we are using z and g only for the prediction of beta, but we might also use them for the other non-linear parameters (provided that the resulting model is still scientifically reasonable).

By default, non-linear covariates are treated as real vectors in Stan. However, if the data of the covariates is of type 'integer' in R (which can be enforced by the 'as.integer' function), the Stan type will be changed to an integer array. That way, covariates can also be used for indexing purposes in Stan.

Non-linear models may not be uniquely identified and / or show bad convergence. For this reason it is mandatory to specify priors on the non-linear parameters. For instructions on how to do that, see set_prior. For some examples of non-linear models, see vignette("brms_nonlinear").

Formula syntax for predicting distributional parameters

It is also possible to predict parameters of the response distribution such as the residual standard deviation sigma in gaussian models or the hurdle probability hu in hurdle models. The syntax closely resembles that of a non-linear parameter, for instance sigma ~ x + s(z) + (1+x|g). For some examples of distributional models, see vignette("brms_distreg").

Parameter mu exists for every family and can be used as an alternative to specifying terms in formula. If both mu and formula are given, the right-hand side of formula is ignored. Accordingly, specifying terms on the right-hand side of both formula and mu at the same time is deprecated. In future versions, formula might be updated by mu.

The following are distributional parameters of specific families (all other parameters are treated as non-linear parameters): sigma (residual standard deviation or scale of the gaussian, student, skew_normal, lognormal exgaussian, and asym_laplace families); shape (shape parameter of the Gamma, weibull, negbinomial, and related zero-inflated / hurdle families); nu (degrees of freedom parameter of the student and frechet families); phi (precision parameter of the beta and zero_inflated_beta families); kappa (precision parameter of the von_mises family); beta (mean parameter of the exponential component of the exgaussian family); quantile (quantile parameter of the asym_laplace family); zi (zero-inflation probability); hu (hurdle probability); zoi (zero-one-inflation probability); coi (conditional one-inflation probability); disc (discrimination) for ordinal models; bs, ndt, and bias (boundary separation, non-decision time, and initial bias of the wiener diffusion model). By default, distributional parameters are modeled on the log scale if they can be positive only or on the logit scale if the can only be within the unit interval.

Alternatively, one may fix distributional parameters to certain values. However, this is mainly useful when models become too complicated and otherwise have convergence issues. We thus suggest to be generally careful when making use of this option. The quantile parameter of the asym_laplace distribution is a good example where it is useful. By fixing quantile, one can perform quantile regression for the specified quantile. For instance, quantile = 0.25 allows predicting the 25%-quantile. Furthermore, the bias parameter in drift-diffusion models, is assumed to be 0.5 (i.e. no bias) in many applications. To achieve this, simply write bias = 0.5. Other possible applications are the Cauchy distribution as a special case of the Student-t distribution with nu = 1, or the geometric distribution as a special case of the negative binomial distribution with shape = 1. Furthermore, the parameter disc ('discrimination') in ordinal models is fixed to 1 by default and not estimated, but may be modeled as any other distributional parameter if desired (see examples). For reasons of identification, 'disc' can only be positive, which is achieved by applying the log-link.

In categorical models, distributional parameters do not have fixed names. Instead, they are named after the response categories (excluding the first one, which serves as the reference category), with the prefix 'mu'. If, for instance, categories are named cat1, cat2, and cat3, the distributional parameters will be named mucat2 and mucat3.

Some distributional parameters currently supported by brmsformula have to be positive (a negative standard deviation or precision parameter does not make any sense) or are bounded between 0 and 1 (for zero-inflated / hurdle probabilities, quantiles, or the initial bias parameter of drift-diffusion models). However, linear predictors can be positive or negative, and thus the log link (for positive parameters) or logit link (for probability parameters) are used by default to ensure that distributional parameters are within their valid intervals. This implies that, by default, effects for such distributional parameters are estimated on the log / logit scale and one has to apply the inverse link function to get to the effects on the original scale. Alternatively, it is possible to use the identity link to predict parameters on their original scale, directly. However, this is much more likely to lead to problems in the model fitting, if the parameter actually has a restricted range.

See also brmsfamily for an overview of valid link functions.

Formula syntax for mixture models

The specification of mixture models closely resembles that of non-mixture models. If not specified otherwise (see below), all mean parameters of the mixture components are predicted using the right-hand side of formula. All types of predictor terms allowed in non-mixture models are allowed in mixture models as well.

Distributional parameters of mixture distributions have the same name as those of the corresponding ordinary distributions, but with a number at the end to indicate the mixture component. For instance, if you use family mixture(gaussian, gaussian), the distributional parameters are sigma1 and sigma2. Distributional parameters of the same class can be fixed to the same value. For the above example, we could write sigma2 = "sigma1" to make sure that both components have the same residual standard deviation, which is in turn estimated from the data.

In addition, there are two types of special distributional parameters. The first are named mu<ID>, that allow for modeling different predictors for the mean parameters of different mixture components. For instance, if you want to predict the mean of the first component using predictor x and the mean of the second component using predictor z, you can write mu1 ~ x as well as mu2 ~ z. The second are named theta<ID>, which constitute the mixing proportions. If the mixing proportions are fixed to certain values, they are internally normalized to form a probability vector. If one seeks to predict the mixing proportions, all but one of the them has to be predicted, while the remaining one is used as the reference category to identify the model. The so-called 'softmax' transformation is applied on the linear predictor terms to form a probability vector.

For more information on mixture models, see the documentation of mixture.

Formula syntax for multivariate models

Multivariate models may be specified using mvbind notation or with help of the mvbf function. Suppose that y1 and y2 are response variables and x is a predictor. Then mvbind(y1, y2) ~ x specifies a multivariate model. The effects of all terms specified at the RHS of the formula are assumed to vary across response variables. For instance, two parameters will be estimated for x, one for the effect on y1 and another for the effect on y2. This is also true for group-level effects. When writing, for instance, mvbind(y1, y2) ~ x + (1+x|g), group-level effects will be estimated separately for each response. To model these effects as correlated across responses, use the ID syntax (see above). For the present example, this would look as follows: mvbind(y1, y2) ~ x + (1+x|2|g). Of course, you could also use any value other than 2 as ID.

It is also possible to specify different formulas for different responses. If, for instance, y1 should be predicted by x and y2 should be predicted by z, we could write mvbf(y1 ~ x, y2 ~ z). Alternatively, multiple brmsformula objects can be added to specify a joint multivariate model (see 'Examples').

Value

An object of class brmsformula, which is essentially a list containing all model formulas as well as some additional information.

See Also

mvbrmsformula, brmsformula-helpers

Examples

# multilevel model with smoothing terms
brmsformula(y ~ x1*x2 + s(z) + (1+x1|1) + (1|g2))

# additionally predict 'sigma'
brmsformula(y ~ x1*x2 + s(z) + (1+x1|1) + (1|g2),
            sigma ~ x1 + (1|g2))

# use the shorter alias 'bf'
(formula1 <- brmsformula(y ~ x + (x|g)))
(formula2 <- bf(y ~ x + (x|g)))
# will be TRUE
identical(formula1, formula2)

# incorporate censoring
bf(y | cens(censor_variable) ~ predictors)

# define a simple non-linear model
bf(y ~ a1 - a2^x, a1 + a2 ~ 1, nl = TRUE)

# predict a1 and a2 differently
bf(y ~ a1 - a2^x, a1 ~ 1, a2 ~ x + (x|g), nl = TRUE)

# correlated group-level effects across parameters
bf(y ~ a1 - a2^x, a1 ~ 1 + (1 |2| g), a2 ~ x + (x |2| g), nl = TRUE)
# alternative but equivalent way to specify the above model
bf(y ~ a1 - a2^x, a1 ~ 1 + (1 | gr(g, id = 2)),
   a2 ~ x + (x | gr(g, id = 2)), nl = TRUE)

# define a multivariate model
bf(mvbind(y1, y2) ~ x * z + (1|g))

# define a zero-inflated model
# also predicting the zero-inflation part
bf(y ~ x * z + (1+x|ID1|g), zi ~ x + (1|ID1|g))

# specify a predictor as monotonic
bf(y ~ mo(x) + more_predictors)

# for ordinal models only
# specify a predictor as category specific
bf(y ~ cs(x) + more_predictors)
# add a category specific group-level intercept
bf(y ~ cs(x) + (cs(1)|g))
# specify parameter 'disc'
bf(y ~ person + item, disc ~ item)

# specify variables containing measurement error
bf(y ~ me(x, sdx))

# specify predictors on all parameters of the wiener diffusion model
# the main formula models the drift rate 'delta'
bf(rt | dec(decision) ~ x, bs ~ x, ndt ~ x, bias ~ x)

# fix the bias parameter to 0.5
bf(rt | dec(decision) ~ x, bias = 0.5)

# specify different predictors for different mixture components
mix <- mixture(gaussian, gaussian)
bf(y ~ 1, mu1 ~ x, mu2 ~ z, family = mix)

# fix both residual standard deviations to the same value
bf(y ~ x, sigma2 = "sigma1", family = mix)

# use the '+' operator to specify models
bf(y ~ 1) +
  nlf(sigma ~ a * exp(b * x), a ~ x) +
  lf(b ~ z + (1|g), dpar = "sigma") +
  gaussian()

# specify a multivariate model using the '+' operator
bf(y1 ~ x + (1|g)) +
  gaussian() + cor_ar(~1|g) +
  bf(y2 ~ z) + poisson()

# specify correlated residuals of a gaussian and a poisson model
form1 <- bf(y1 ~ 1 + x + (1|c|obs), sigma = 1) + gaussian()
form2 <- bf(y2 ~ 1 + x + (1|c|obs)) + poisson()

# model missing values in predictors
bf(bmi ~ age * mi(chl)) +
  bf(chl | mi() ~ age) +
  set_rescor(FALSE)

# model sigma as a function of the mean
bf(y ~ eta, nl = TRUE) +
  lf(eta ~ 1 + x) +
  nlf(sigma ~ tau * sqrt(eta)) +
  lf(tau ~ 1)

Description

Helper functions to specify linear and non-linear formulas for use with brmsformula.

Usage

nlf(formula, ..., flist = NULL, dpar = NULL, resp = NULL, loop = NULL)

lf(
  ...,
  flist = NULL,
  dpar = NULL,
  resp = NULL,
  center = NULL,
  cmc = NULL,
  sparse = NULL,
  decomp = NULL
)

acformula(autocor, resp = NULL)

set_nl(nl = TRUE, dpar = NULL, resp = NULL)

set_rescor(rescor = TRUE)

set_mecor(mecor = TRUE)

Arguments

Value

For lf and nlf a list that can be passed to brmsformula or added to an existing brmsformula or mvbrmsformula object. For set_nl and set_rescor a logical value that can be added to an existing brmsformula or mvbrmsformula object.

See Also

brmsformula, mvbrmsformula

Examples

# add more formulas to the model
bf(y ~ 1) +
  nlf(sigma ~ a * exp(b * x)) +
  lf(a ~ x, b ~ z + (1|g)) +
  gaussian()

# specify 'nl' later on
bf(y ~ a * inv_logit(x * b)) +
  lf(a + b ~ z) +
  set_nl(TRUE)

# specify a multivariate model
bf(y1 ~ x + (1|g)) +
  bf(y2 ~ z) +
  set_rescor(TRUE)

# add autocorrelation terms
bf(y ~ x) + acformula(~ arma(p = 1, q = 1) + car(W))

Description

A brmshypothesis object contains posterior draws as well as summary statistics of non-linear hypotheses as returned by hypothesis.

Usage

## S3 method for class 'brmshypothesis'
print(x, digits = 2, chars = 20, ...)

## S3 method for class 'brmshypothesis'
plot(
  x,
  nvariables = 5,
  N = NULL,
  ignore_prior = FALSE,
  chars = 40,
  colors = NULL,
  theme = NULL,
  ask = TRUE,
  plot = TRUE,
  ...
)

Arguments

Details

The two most important elements of a brmshypothesis object are hypothesis, which is a data.frame containing the summary estimates of the hypotheses, and samples, which is a data.frame containing the corresponding posterior draws.

See Also

hypothesis

Description

Parse formulas objects for use in brms.

Usage

brmsterms(formula, ...)

## Default S3 method:
brmsterms(formula, ...)

## S3 method for class 'brmsformula'
brmsterms(formula, check_response = TRUE, resp_rhs_all = TRUE, ...)

## S3 method for class 'mvbrmsformula'
brmsterms(formula, ...)

Arguments

Details

This is the main formula parsing function of brms. It should usually not be called directly, but is exported to allow package developers making use of the formula syntax implemented in brms. As long as no other packages depend on this functions, it may be changed without deprecation warnings, when new features make this necessary.

Value

An object of class brmsterms or mvbrmsterms (for multivariate models), which is a list containing all required information initially stored in formula in an easier to use format, basically a list of formulas (not an abstract syntax tree).

See Also

brm, brmsformula, mvbrmsformula

Description

Set up an spatial conditional autoregressive (CAR) term in brms. The function does not evaluate its arguments – it exists purely to help set up a model with CAR terms.

Usage

car(M, gr = NA, type = "escar")

Arguments

Details

The escar and esicar types are implemented based on the case study of Max Joseph (https://github.com/mbjoseph/CARstan). The icar and bym2 type is implemented based on the case study of Mitzi Morris (https://mc-stan.org/users/documentation/case-studies/icar_stan.html).

Value

An object of class 'car_term', which is a list of arguments to be interpreted by the formula parsing functions of brms.

See Also

autocor-terms

Examples

## Not run: 
# generate some spatial data
east <- north <- 1:10
Grid <- expand.grid(east, north)
K <- nrow(Grid)

# set up distance and neighbourhood matrices
distance <- as.matrix(dist(Grid))
W <- array(0, c(K, K))
W[distance == 1] <- 1
rownames(W) <- 1:nrow(W)

# generate the covariates and response data
x1 <- rnorm(K)
x2 <- rnorm(K)
theta <- rnorm(K, sd = 0.05)
phi <- rmulti_normal(
  1, mu = rep(0, K), Sigma = 0.4 * exp(-0.1 * distance)
)
eta <- x1 + x2 + phi
prob <- exp(eta) / (1 + exp(eta))
size <- rep(50, K)
y <- rbinom(n = K, size = size, prob = prob)
g <- 1:length(y)
dat <- data.frame(y, size, x1, x2, g)

# fit a CAR model
fit <- brm(y | trials(size) ~ x1 + x2 + car(W, gr = g),
           data = dat, data2 = list(W = W),
           family = binomial())
summary(fit)

## End(Not run)

Description

Extract model coefficients, which are the sum of population-level effects and corresponding group-level effects

Usage

## S3 method for class 'brmsfit'
coef(object, summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)

Arguments

Value

A list of 3D arrays (one per grouping factor). If summary is TRUE, the 1st dimension contains the factor levels, the 2nd dimension contains the summary statistics (see posterior_summary), and the 3rd dimension contains the group-level effects. If summary is FALSE, the 1st dimension contains the posterior draws, the 2nd dimension contains the factor levels, and the 3rd dimension contains the group-level effects.

Examples

## Not run: 
fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit),
           data = epilepsy, family = gaussian(), chains = 2)
## extract population and group-level coefficients separately
fixef(fit)
ranef(fit)
## extract combined coefficients
coef(fit)

## End(Not run)

Description

Combine multiple brmsfit objects, which fitted the same model. This is usefully for instance when having manually run models in parallel.

Usage

combine_models(..., mlist = NULL, check_data = TRUE)

Arguments

Details

This function just takes the first model and replaces its stanfit object (slot fit) by the combined stanfit objects of all models.

Value

A brmsfit object.

Description

Compare information criteria of different models fitted with waic or loo. Deprecated and will be removed in the future. Please use loo_compare instead.

Usage

compare_ic(..., x = NULL, ic = c("loo", "waic", "kfold"))

Arguments

Details

See loo_compare for the recommended way of comparing models with the loo package.

Value

An object of class iclist.

See Also

loo, loo_compare add_criterion

Examples

## Not run: 
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
            data = inhaler)
waic1 <- waic(fit1)

# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
            data = inhaler)
waic2 <- waic(fit2)

# compare both models
compare_ic(waic1, waic2)

## End(Not run)

Description

Display conditional effects of one or more numeric and/or categorical predictors including two-way interaction effects.

Usage

## S3 method for class 'brmsfit'
conditional_effects(
  x,
  effects = NULL,
  conditions = NULL,
  int_conditions = NULL,
  re_formula = NA,
  prob = 0.95,
  robust = TRUE,
  method = "posterior_epred",
  spaghetti = FALSE,
  surface = FALSE,
  categorical = FALSE,
  ordinal = FALSE,
  transform = NULL,
  resolution = 100,
  select_points = 0,
  too_far = 0,
  probs = NULL,
  ...
)

conditional_effects(x, ...)

## S3 method for class 'brms_conditional_effects'
plot(
  x,
  ncol = NULL,
  points = getOption("brms.plot_points", FALSE),
  rug = getOption("brms.plot_rug", FALSE),
  mean = TRUE,
  jitter_width = 0,
  stype = c("contour", "raster"),
  line_args = list(),
  cat_args = list(),
  errorbar_args = list(),
  surface_args = list(),
  spaghetti_args = list(),
  point_args = list(),
  rug_args = list(),
  facet_args = list(),
  theme = NULL,
  ask = TRUE,
  plot = TRUE,
  ...
)

Arguments

Details

When creating conditional_effects for a particular predictor (or interaction of two predictors), one has to choose the values of all other predictors to condition on. By default, the mean is used for continuous variables and the reference category is used for factors, but you may change these values via argument conditions. This also has an implication for the points argument: In the created plots, only those points will be shown that correspond to the factor levels actually used in the conditioning, in order not to create the false impression of bad model fit, where it is just due to conditioning on certain factor levels.

To fully change colors of the created plots, one has to amend both scale_colour and scale_fill. See scale_colour_grey or scale_colour_gradient for more details.

Value

An object of class 'brms_conditional_effects' which is a named list with one data.frame per effect containing all information required to generate conditional effects plots. Among others, these data.frames contain some special variables, namely estimate__ (predicted values of the response), se__ (standard error of the predicted response), lower__ and upper__ (lower and upper bounds of the uncertainty interval of the response), as well as cond__ (used in faceting when conditions contains multiple rows).

The corresponding plot method returns a named list of ggplot objects, which can be further customized using the ggplot2 package.

Examples

## Not run: 
fit <- brm(count ~ zAge + zBase * Trt + (1 | patient),
           data = epilepsy, family = poisson())

## plot all conditional effects
plot(conditional_effects(fit), ask = FALSE)

## change colours to grey scale
library(ggplot2)
ce <- conditional_effects(fit, "zBase:Trt")
plot(ce, plot = FALSE)[[1]] +
  scale_color_grey() +
  scale_fill_grey()

## only plot the conditional interaction effect of 'zBase:Trt'
## for different values for 'zAge'
conditions <- data.frame(zAge = c(-1, 0, 1))
plot(conditional_effects(fit, effects = "zBase:Trt",
                         conditions = conditions))

## also incorporate group-level effects variance over patients
## also add data points and a rug representation of predictor values
plot(conditional_effects(fit, effects = "zBase:Trt",
                         conditions = conditions, re_formula = NULL),
     points = TRUE, rug = TRUE)

## change handling of two-way interactions
int_conditions <- list(
  zBase = setNames(c(-2, 1, 0), c("b", "c", "a"))
)
conditional_effects(fit, effects = "Trt:zBase",
                    int_conditions = int_conditions)
conditional_effects(fit, effects = "Trt:zBase",
                    int_conditions = list(zBase = quantile))

## fit a model to illustrate how to plot 3-way interactions
fit3way <- brm(count ~ zAge * zBase * Trt, data = epilepsy)
conditions <- make_conditions(fit3way, "zAge")
conditional_effects(fit3way, "zBase:Trt", conditions = conditions)
## only include points close to the specified values of zAge
ce <- conditional_effects(
  fit3way, "zBase:Trt", conditions = conditions,
  select_points = 0.1
)
plot(ce, points = TRUE)

## End(Not run)

Description

Display smooth s and t2 terms of models fitted with brms.

Usage

## S3 method for class 'brmsfit'
conditional_smooths(
  x,
  smooths = NULL,
  int_conditions = NULL,
  prob = 0.95,
  spaghetti = FALSE,
  resolution = 100,
  too_far = 0,
  ndraws = NULL,
  draw_ids = NULL,
  nsamples = NULL,
  subset = NULL,
  probs = NULL,
  ...
)

conditional_smooths(x, ...)

Arguments

Details

Two-dimensional smooth terms will be visualized using either contour or raster plots.

Value

For the brmsfit method, an object of class brms_conditional_effects. See conditional_effects for more details and documentation of the related plotting function.

Examples

## Not run: 
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
fit <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
# show all smooth terms
plot(conditional_smooths(fit), rug = TRUE, ask = FALSE)
# show only the smooth term s(x2)
plot(conditional_smooths(fit, smooths = "s(x2)"), ask = FALSE)

# fit and plot a two-dimensional smooth term
fit2 <- brm(y ~ t2(x0, x2), data = dat)
ms <- conditional_smooths(fit2)
plot(ms, stype = "contour")
plot(ms, stype = "raster")

## End(Not run)

Description

Function used to set up constant priors in brms. The function does not evaluate its arguments – it exists purely to help set up the model.

Usage

constant(const, broadcast = TRUE)

Arguments

Value

A named list with elements const and broadcast.

See Also

set_prior

Examples

stancode(count ~ Base + Age, data = epilepsy,
         prior = prior(constant(1), class = "b"))

# will fail parsing because brms will try to broadcast a vector into a vector
stancode(count ~ Base + Age, data = epilepsy,
         prior = prior(constant(alpha), class = "b"),
         stanvars = stanvar(c(1, 0), name = "alpha"))

stancode(count ~ Base + Age, data = epilepsy,
         prior = prior(constant(alpha, broadcast = FALSE), class = "b"),
         stanvars = stanvar(c(1, 0), name = "alpha"))

Description

Extract control parameters of the NUTS sampler such as adapt_delta or max_treedepth.

Usage

control_params(x, ...)

## S3 method for class 'brmsfit'
control_params(x, pars = NULL, ...)

Arguments

Value

A named list with control parameter values.

Description

This function is deprecated. Please see ar for the new syntax. This function is a constructor for the cor_arma class, allowing for autoregression terms only.

Usage

cor_ar(formula = ~1, p = 1, cov = FALSE)

Arguments

Details

AR refers to autoregressive effects of residuals, which is what is typically understood as autoregressive effects. However, one may also model autoregressive effects of the response variable, which is called ARR in brms.

Value

An object of class cor_arma containing solely autoregression terms.

See Also

cor_arma

Examples

cor_ar(~visit|patient, p = 2)

Description

This function is deprecated. Please see arma for the new syntax. This functions is a constructor for the cor_arma class, representing an autoregression-moving average correlation structure of order (p, q).

Usage

cor_arma(formula = ~1, p = 0, q = 0, r = 0, cov = FALSE)

Arguments

Value

An object of class cor_arma, representing an autoregression-moving-average correlation structure.

See Also

cor_ar, cor_ma

Examples