brm_model {brms.mmrm} | R Documentation |
Fit an MMRM.
Description
Fit an MMRM model using brms
.
Usage
brm_model(
data,
formula,
...,
prior = NULL,
family = brms::brmsfamily(family = "gaussian")
)
Arguments
data |
A classed data frame from |
formula |
An object of class |
... |
Arguments to |
prior |
Either |
family |
A |
Value
A fitted model object from brms
.
Parameterization
For a formula on a brm_data()
dataset,
the formula is not the only factor
that determines the fixed effect mapping.
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()
,
brm_formula_sigma()
Examples
if (identical(Sys.getenv("BRM_EXAMPLES", unset = ""), "true")) {
set.seed(0L)
data <- brm_data(
data = brm_simulate_simple()$data,
outcome = "response",
role = "response",
group = "group",
time = "time",
patient = "patient",
reference_group = "group_1",
reference_time = "time_1"
)
formula <- brm_formula(
data = data,
baseline = FALSE,
baseline_time = FALSE
)
# Optional: set the contrast option, which determines the model matrix.
options(contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
# See the fixed effect mapping you get from the data:
head(brms::make_standata(formula = formula, data = data)$X)
# Specify a different contrast method to use an alternative
# mapping 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.
suppressWarnings(print(model))
# The `prior_summary()` function shows the full prior specification
# which reflects the fully realized fixed effects mapping.
brms::prior_summary(model)
}