brms (version 2.4.0)

brm_multiple: 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.

Usage

brm_multiple(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,
  combine = TRUE, seed = NA, ...)

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.

autocor

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.

cov_ranef

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.

sample_prior

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. If set to "only", samples are drawn solely from the priors ignoring the likelihood. In this case, all parameters must have proper priors.

sparse

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.

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.

combine

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

seed

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

...

Further arguments passed to brm.

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.

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.

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
# }
# NOT RUN {
# }