Internally, it creates a count version of the individual-data via ccesMRPprep::build_counts and then runs the regression in fit_brms_binomial.

fit_brms(
.formula,
.data,
name_ones_as = "yes",
name_trls_as = "n_response",
...
)

## Arguments

.formula

Formula in binary y ~ (1|x1) + (1|x2) form.

.data

Individual-level dataset

name_ones_as

The name for the variable name for the number of successes

name_trls_as

The name for the variable name of the number of trials

...

Arguments passed on to fit_brms_binomial

.prior

prior specification that can be interpreted by brms. The default is a standard normal prior, which is tighter than the brms default but has shown to have good prior posterior draws

.cores

Number of cores to uses

.chains

Number of chains to pass on fit_brms

.iter

Number of total iterations.

.warmup

Of the iterations, how much are burn-ins. Defaults to half.

verbose

Whether to show iteration messages

.seed

seed for randomization to pass into brm

.backend

The backend argument of brms. Defaults to "rstan", can also be "cmdstanr"

## Examples

if (FALSE) {
fit <- fit_brms(response ~ (1|educ) + (1|cd), cces_GA)
}