brms: stanvar – R documentation

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stanvar

User-defined variables passed to Stan

Description

Prepare user-defined variables to be passed to one of Stan's program blocks. This is primarily useful for defining more complex priors, for refitting models without recompilation despite changing priors, or for defining custom Stan functions.

Usage

stanvar(
  x = NULL,
  name = NULL,
  scode = NULL,
  block = "data",
  position = "start",
  pll_args = NULL
)

Arguments

`x`	An R object containing data to be passed to Stan. Only required if `block = 'data'` and ignored otherwise.
`name`	Optional character string providing the desired variable name of the object in `x`. If `NULL` (the default) the variable name is directly inferred from `x`.
`scode`	Line of Stan code to define the variable in Stan language. If `block = 'data'`, the Stan code is inferred based on the class of `x` by default.
`block`	Name of one of Stan's program blocks in which the variable should be defined. Can be `'data'`, `'tdata'` (transformed data), `'parameters'`, `'tparameters'` (transformed parameters), `'model'`, `'likelihood'` (part of the model block where the likelihood is given), `'genquant'` (generated quantities) or `'functions'`.
`position`	Name of the position within the block where the Stan code should be placed. Currently allowed are `'start'` (the default) and `'end'` of the block.
`pll_args`	Optional Stan code to be put into the header of `partial_log_lik` functions. This ensures that the variables specified in `scode` can be used in the likelihood even when within-chain parallelization is activated via `threading`.

Value

An object of class stanvars.

Examples

bprior <- prior(normal(mean_intercept, 10), class = "Intercept")
stanvars <- stanvar(5, name = "mean_intercept")
make_stancode(count ~ Trt, epilepsy, prior = bprior, 
              stanvars = stanvars)
              
# define a multi-normal prior with known covariance matrix
bprior <- prior(multi_normal(M, V), class = "b")
stanvars <- stanvar(rep(0, 2), "M", scode = "  vector[K] M;") +
  stanvar(diag(2), "V", scode = "  matrix[K, K] V;") 
make_stancode(count ~ Trt + zBase, epilepsy,
              prior = bprior, stanvars = stanvars)
              
# define a hierachical prior on the regression coefficients
bprior <- set_prior("normal(0, tau)", class = "b") +
  set_prior("target += normal_lpdf(tau | 0, 10)", check = FALSE)
stanvars <- stanvar(scode = "real<lower=0> tau;", 
                    block = "parameters")
make_stancode(count ~ Trt + zBase, epilepsy,
              prior = bprior, stanvars = stanvars)
              
# ensure that 'tau' is passed to the likelihood of a threaded model
# not necessary for this example but may be necessary in other cases
stanvars <- stanvar(scode = "real<lower=0> tau;", 
                    block = "parameters", pll_args = "real tau")
make_stancode(count ~ Trt + zBase, epilepsy,
              stanvars = stanvars, threads = threading(2))

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10