bayesian {bayesian}  R Documentation 
General Interface for Bayesian TidyModels
Description
bayesian()
is a way to generate a specification of a model
before fitting and allows the model to be created using
Stan via brms package in R.
Usage
bayesian(
mode = "regression",
engine = "brms",
formula.override = NULL,
family = NULL,
prior = NULL,
sample_prior = NULL,
knots = NULL,
stanvars = NULL,
fit = NULL,
init = NULL,
chains = NULL,
iter = NULL,
warmup = NULL,
thin = NULL,
cores = NULL,
threads = NULL,
algorithm = NULL,
backend = NULL,
stan_args = NULL,
control = NULL,
save_pars = NULL,
save_model = NULL,
file = NULL,
file_refit = NULL,
normalize = NULL,
future = NULL,
seed = NULL,
silent = NULL
)
## S3 method for class 'bayesian'
update(
object,
parameters = NULL,
formula.override = NULL,
family = NULL,
prior = NULL,
sample_prior = NULL,
knots = NULL,
stanvars = NULL,
fit = NULL,
init = NULL,
chains = NULL,
iter = NULL,
warmup = NULL,
thin = NULL,
cores = NULL,
threads = NULL,
algorithm = NULL,
backend = NULL,
stan_args = NULL,
control = NULL,
save_pars = NULL,
save_model = NULL,
file = NULL,
file_refit = NULL,
normalize = NULL,
future = NULL,
seed = NULL,
silent = NULL,
fresh = FALSE,
...
)
bayesian_fit(formula, data, ...)
bayesian_formula(formula, ...)
bayesian_terms(formula, ...)
bayesian_family(family, ...)
bayesian_predict(object, ...)
bayesian_write(object, file)
bayesian_read(file)
Arguments
mode 
A single character string for the prediction outcome mode.
Possible values for this model are "unknown", "regression", or
"classification".

engine 
A single character string specifying what computational
engine to use for fitting. Possible engines are listed below.
The default for this model is "brms" .

formula.override 
Overrides the formula; for details see
brmsformula .

family 
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.

prior 
One or more brmsprior objects created by
set_prior or related functions and combined using the
c method or the + operator. See also default_prior
for more help.

sample_prior 
Indicate if draws from priors should be drawn
additionally to the posterior draws. Options are "no" (the
default), "yes" , and "only" . Among others, these draws 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 draws for the overall intercept are not obtained by default
for technical reasons. See brmsformula how to obtain prior
draws for the intercept. If sample_prior is set to "only" ,
draws are drawn solely from the priors ignoring the likelihood, which
allows among others to generate draws from the prior predictive
distribution. In this case, all parameters must have proper priors.

knots 
Optional list containing user specified knot values to be used
for basis construction of smoothing terms. See
gamm for more details.

stanvars 
An optional stanvars object generated by function
stanvar to define additional variables for use in
Stan's program blocks.

fit 
An instance of S3 class brmsfit derived from a previous
fit; defaults to NA . If fit is of class brmsfit , the
compiled model associated with the fitted result is reused and all
arguments modifying the model code or data are ignored. It is not
recommended to use this argument directly, but to call the
update method, instead.

init 
Initial values for the sampler. If NULL (the default) or
"random" , Stan will randomly generate initial values for parameters
in a reasonable range. If 0 , all parameters are initialized to zero
on the unconstrained space. This option is sometimes useful for certain
families, as it happens that default random initial values cause draws to
be essentially constant. Generally, setting init = 0 is worth a try,
if chains do not initialize or behave well. Alternatively, init can
be a list of lists containing the initial values, or a function (or
function name) generating initial values. The latter options are mainly
implemented for internal testing but are available to users if necessary.
If specifying initial values using a list or a function then currently the
parameter names must correspond to the names used in the generated Stan
code (not the names used in R). For more details on specifying initial
values you can consult the documentation of the selected backend .

chains 
Number of Markov chains (defaults to 4).

iter 
Number of total iterations per chain (including warmup; defaults
to 2000).

warmup 
A positive integer specifying number of warmup (aka burnin)
iterations. This also specifies the number of iterations used for stepsize
adaptation, so warmup draws should not be used for inference. The number
of warmup should not be larger than iter and the default is
iter/2 .

thin 
Thinning rate. Must be a positive integer. Set thin > 1 to
save memory and computation time if iter is large.

cores 
Number of cores to use when executing the chains in parallel,
which defaults to 1 but we recommend setting the mc.cores option to
be as many processors as the hardware and RAM allow (up to the number of
chains). For nonWindows OS in noninteractive R sessions, forking is used
instead of PSOCK clusters.

threads 
Number of threads to use in withinchain parallelization. For
more control over the threading process, threads may also be a
brmsthreads object created by threading . Withinchain
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. Can be set globally for
the current R session via the "brms.threads" option (see
options ).

algorithm 
Character string naming the estimation approach to use.
Options are "sampling" for MCMC (the default), "meanfield" for
variational inference with independent normal distributions,
"fullrank" for variational inference with a multivariate normal
distribution, or "fixed_param" for sampling from fixed parameter
values. Can be set globally for the current R session via the
"brms.algorithm" option (see options ).

backend 
Character string naming the package to use as the backend for
fitting the Stan model. Options are "rstan" (the default) or
"cmdstanr" . Can be set globally for the current R session via the
"brms.backend" option (see options ). Details on the
rstan and cmdstanr packages are available at
https://mcstan.org/rstan/ and https://mcstan.org/cmdstanr/,
respectively. Additionally a "mock" backend is available to make
testing brms and packages that depend on it easier.
The "mock" backend does not actually do any fitting, it only checks
the generated Stan code for correctness and then returns whatever is passed
in an additional mock_fit argument as the result of the fit.

stan_args 
A list of extra arguments to Stan.

control 
A named list of parameters to control the sampler's
behavior. It defaults to NULL so all the default values are used.
The most important control parameters are discussed in the 'Details'
section below. For a comprehensive overview see
stan .

save_pars 
An object generated by save_pars controlling
which parameters should be saved in the model. The argument has no
impact on the model fitting itself.

save_model 
Either NULL or a character string. In the latter
case, the model's Stan code is saved via cat in a text file
named after the string supplied in save_model .

file 
A character string of the file path to brmsfit object
saved via saveRDS .

file_refit 
Modifies when the fit stored via the file argument
is reused. Can be set globally for the current R session via the
"brms.file_refit" option (see options ).
For "never" (default) the fit is always loaded if it
exists and fitting is skipped. For "always" the model is always
refitted. If set to "on_change" , brms will
refit the model if model, data or algorithm as passed to Stan differ from
what is stored in the file. This also covers changes in priors,
sample_prior , stanvars , covariance structure, etc. If you
believe there was a false positive, you can use
brmsfit_needs_refit to see why refit is deemed necessary.
Refit will not be triggered for changes in additional parameters of the fit
(e.g., initial values, number of iterations, control arguments, ...). A
known limitation is that a refit will be triggered if withinchain
parallelization is switched on/off.

normalize 
Logical. Indicates whether normalization constants should
be included in the Stan code (defaults to TRUE ). Setting it
to FALSE requires Stan version >= 2.25 to work. If FALSE ,
sampling efficiency may be increased but some post processing functions
such as bridge_sampler will not be available. Can be
controlled globally for the current R session via the 'brms.normalize'
option.

future 
Logical; If TRUE , the future
package is used for parallel execution of the chains and argument
cores will be ignored. Can be set globally for the current R
session via the "future" option. The execution type is controlled via
plan (see the examples section below).

seed 
The seed for random number generation to make results
reproducible. If NA (the default), Stan will set the seed
randomly.

silent 
Verbosity level between 0 and 2 .
If 1 (the default), most of the
informational messages of compiler and sampler are suppressed.
If 2 , even more messages are suppressed. The actual
sampling progress is still printed. Set refresh = 0 to turn this off
as well. If using backend = "rstan" you can also set
open_progress = FALSE to prevent opening additional progress bars.

object 
A Bayesian model specification.

parameters 
A 1row tibble or named list with main
parameters to update. If the individual arguments are used,
these will supersede the values in parameters . Also, using
engine arguments in this object will result in an error.

fresh 
A logical for whether the arguments should be
modified inplace of or replaced wholesale.

... 
Other arguments passed to internal functions.

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 
An object of class data.frame (or one that can be coerced
to that class) containing data of all variables used in the model.

Details
The arguments are converted to their specific names at the
time that the model is fit. Other options and argument can be
set using set_engine()
. If left to their defaults
here (NULL
), the values are taken from the underlying model
functions. If parameters need to be modified, update()
can be
used in lieu of recreating the object from scratch.
The data given to the function are not saved and are only used
to determine the mode of the model. For bayesian()
, the
possible modes are "regression" and "classification".
The model can be created by the fit()
function using the
following engines:
Value
An updated model specification.
Engine Details
Engines may have preset default arguments when executing the model fit
call. For this type of model, the template of the fit calls are:
bayesian() >
set_engine("brms") >
translate()
## Bayesian Model Specification (regression)
##
## Computational engine: brms
##
## Model fit template:
## bayesian::bayesian_fit(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg())
See Also
brm
,
brmsfit
,
update.brmsfit
,
predict.brmsfit
,
posterior_epred.brmsfit
,
posterior_predict.brmsfit
,
brmsformula
,
brmsformulahelpers
,
brmsterms
,
brmsfamily
,
customfamily
,
family
,
formula
,
update.formula
.
Examples
bayesian()
show_model_info("bayesian")
bayesian(mode = "classification")
bayesian(mode = "regression")
## Not run:
bayesian_mod <
bayesian() >
set_engine("brms") >
fit(
rating ~ treat + period + carry + (1  subject),
data = inhaler
)
summary(bayesian_mod$fit)
## End(Not run)
model < bayesian(init = "random")
model
update(model, init = "0")
update(model, init = "0", fresh = TRUE)
[Package
bayesian version 1.0.1
Index]