Fit a basic MMRM model using `brms`

.

## Usage

```
brm_model(
data,
formula,
...,
prior = NULL,
family = brms::brmsfamily(family = "gaussian")
)
```

## Arguments

- data
A tidy data frame with one row per patient per discrete time point.

- formula
An object of class

`"brmsformula"`

from`brm_formula()`

or`brms::brmsformula()`

. Should include the full parameterization of the model, including fixed effects, residual correlation, and heterogeneity in the discrete-time-specific residual variance components.- ...
Arguments to

`brms::brm()`

other than`data`

,`formula`

, and`prior`

.- prior
Either

`NULL`

for default priors or a`"brmsprior"`

object from`brms::prior()`

.- family
A

`brms`

family object generated by`brms::brmsfamily()`

. Must fit a continuous outcome variable and have the identity link.

## Parameterization

The formula is not the only factor
that determines the fixed effect parameterization.
The ordering of the categorical variables in the data,
as well as the `contrast`

option in R, affect the
construction of the model matrix. To see the model
matrix that will ultimately be used in `brm_model()`

,
run `brms::make_standata()`

and examine the `X`

element
of the returned list. See the examples below for a
demonstration.

## See also

Other models:
`brm_formula()`

## Examples

```
if (identical(Sys.getenv("BRM_EXAMPLES", unset = ""), "true")) {
set.seed(0L)
data <- brm_data(
data = brm_simulate_simple()$data,
outcome = "response",
role = "response",
group = "group",
time = "time",
patient = "patient",
reference_group = "group_1",
reference_time = "time_1"
)
formula <- brm_formula(
data = data,
baseline = FALSE,
baseline_time = FALSE
)
# Optional: set the contrast option, which determines the model matrix.
options(contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
# See the fixed effect parameterization you get from the data:
head(brms::make_standata(formula = formula, data = data)$X)
# Specify a different contrast method to use an alternative
# parameterization when fitting the model with brm_model():
options(
contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")
)
# different model matrix than before:
head(brms::make_standata(formula = formula, data = data)$X)
tmp <- utils::capture.output(
suppressMessages(
suppressWarnings(
model <- brm_model(
data = data,
formula = formula,
chains = 1,
iter = 100,
refresh = 0
)
)
)
)
# The output model is a brms model fit object.
model
# The `prior_summary()` function shows the full prior specification
# which reflects the fully realized fixed effects parameterization.
brms::prior_summary(model)
}
```