Generate data for brms models to be passed to Stan
make_standata(
formula,
data,
family = gaussian(),
prior = NULL,
autocor = NULL,
data2 = NULL,
cov_ranef = NULL,
sample_prior = "no",
stanvars = NULL,
threads = NULL,
knots = NULL,
...
)
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
.
An object of class data.frame
(or one that can be coerced
to that class) containing data of all variables used in the model.
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.
(Deprecated) 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.
It is now recommend to specify autocorrelation terms directly
within formula
. See brmsformula
for more details.
A named list
of objects containing data, which
cannot be passed via argument data
. Required for some objects
used in autocorrelation structures to specify dependency structures
as well as for within-group covariance matrices.
(Deprecated) 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.
It is now recommended to specify those matrices in the formula
interface using the gr
and related functions. See
vignette("brms_phylogenetics")
for more details.
Indicate if samples from priors should be drawn
additionally to the posterior samples. Options are "no"
(the
default), "yes"
, and "only"
. Among others, these samples can
be used to calculate Bayes factors for point hypotheses via
hypothesis
. Please note that improper priors are not sampled,
including the default improper priors used by brm
. See
set_prior
on how to set (proper) priors. Please also note
that prior samples for the overall intercept are not obtained by default
for technical reasons. See brmsformula
how to obtain prior
samples for the intercept. If sample_prior
is set to "only"
,
samples are drawn solely from the priors ignoring the likelihood, which
allows among others to generate samples from the prior predictive
distribution. In this case, all parameters must have proper priors.
An optional stanvars
object generated by function
stanvar
to define additional variables for use in
Stan's program blocks.
Number of threads to use in within-chain parallelization. For
more control over the threading process, threads
may also be a
brmsthreads
object created by threading
. Within-chain
parallelization is experimental! We recommend its use only if you are
experienced with Stan's reduce_sum
function and have a slow running
model that cannot be sped up by any other means.
Optional list containing user specified knot values to be used
for basis construction of smoothing terms. See
gamm
for more details.
Other arguments for internal use.
A named list of objects containing the required data to fit a brms model with Stan.
# NOT RUN {
sdata1 <- make_standata(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "cumulative")
str(sdata1)
sdata2 <- make_standata(count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = "poisson")
str(sdata2)
# }
Run the code above in your browser using DataLab