brms (version 2.1.0)

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, combine = TRUE, ...)

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.

combine

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

...

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 {
# }