brm_multiple {brms}R Documentation

Run the same brms model on multiple datasets

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,
  seed = NA,
  file = NULL,
  file_refit = "never",
  ...
)

Arguments

formula

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.

data

A list of data.frames each of which will be used to fit a separate model. Alternatively, a mids object from the mice package.

family

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.

prior

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.

data2

A list of named lists each of which will be used to fit a separate model. Each of the named lists contains objects representing data which cannot be passed via argument data (see brm for examples). The length of the outer list should match the length of the list passed to the data argument.

autocor

(Deprecated) 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. It is now recommend to specify autocorrelation terms directly within formula. See brmsformula for more details.

cov_ranef

(Deprecated) 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. It is now recommended to specify those matrices in the formula interface using the gr and related functions. See vignette("brms_phylogenetics") for more details.

sample_prior

Indicate if samples from priors should be drawn additionally to the posterior samples. Options are "no" (the default), "yes", and "only". Among others, these samples can be used to calculate Bayes factors for point hypotheses via hypothesis. Please note that improper priors are not sampled, including the default improper priors used by brm. See set_prior on how to set (proper) priors. Please also note that prior samples for the overall intercept are not obtained by default for technical reasons. See brmsformula how to obtain prior samples for the intercept. If sample_prior is 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.

sparse

(Deprecated) 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. It is now recommended to use the sparse argument of brmsformula and related functions.

knots

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

stanvars

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

stan_funs

(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.

silent

Verbosity level between 0 and 2. If 1 (the default), most of the informational messages of compiler and sampler are suppressed. If 2, even more messages are suppressed. The actual sampling progress is still printed. Set refresh = 0 to turn this off as well. If using backend = "rstan" you can also set open_progress = FALSE to prevent opening additional progress bars.

recompile

Logical, indicating whether the Stan model should be recompiled for every imputed data set. Defaults to FALSE. If NULL, brm_multiple tries to figure out internally, if recompilation is necessary, for example because data-dependent priors have changed. Using the default of no recompilation should be fine in most cases.

combine

Logical; Indicates if the fitted models should be combined into a single fitted model object via combine_models. Defaults to TRUE.

fit

An instance of S3 class brmsfit_multiple derived from a previous fit; defaults to NA. If fit is of class brmsfit_multiple, 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.

seed

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

file

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. Unless you specify the file_refit argument as well, the 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.

file_refit

Modifies when the fit stored via the file parameter is re-used. For "never" (default) the fit is always loaded if it exists and fitting is skipped. If set to "on_change", brms will refit the model if model, data or algorithm as passed to Stan differ from what is stored in the file. This also covers changes in priors, sample_prior, stanvars, covariance structure, etc. If you believe there was a false positive, you can use brmsfit_needs_refit to see why refit is deemed necessary. Refit will not be triggered for changes in additional parameters of the fit (e.g., initial values, number of iterations, control arguments, ...). A known limitation is that a refit will be triggered if within-chain parallelization is switched on/off.

...

Further arguments passed to brm.

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, investigate fit$rhats, where fit denotes the output of brm_multiple.

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.

Author(s)

Paul-Christian Buerkner paul.buerkner@gmail.com

Examples

## Not run: 
library(mice)
imp <- mice(nhanes2)

# 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, pars = "^b_")
# investigate convergence of the original models
fit_imp2$rhats

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

## End(Not run)


[Package brms version 2.15.0 Index]