brms (version 2.14.0)

posterior_samples.brmsfit: Extract Posterior Samples

Description

Extract posterior samples of specified parameters.

Usage

# S3 method for brmsfit
posterior_samples(
  x,
  pars = NA,
  fixed = FALSE,
  add_chain = FALSE,
  subset = NULL,
  as.matrix = FALSE,
  as.array = FALSE,
  ...
)
posterior_samples(x, pars = NA, ...)
# S3 method for brmsfit
as.data.frame(x, row.names = NULL, optional = TRUE, ...)
# S3 method for brmsfit
as.matrix(x, ...)
# S3 method for brmsfit
as.array(x, ...)

Arguments

x

An R object typically of class brmsfit

pars

Names of parameters for which posterior samples should be returned, as given by a character vector or regular expressions. By default, all posterior samples of all parameters are extracted.

fixed

Indicates whether parameter names should be matched exactly (TRUE) or treated as regular expressions (FALSE). Default is FALSE.

add_chain

A flag indicating if the returned data.frame should contain two additional columns. The chain column indicates the chain in which each sample was generated, the iter column indicates the iteration number within each chain.

subset

A numeric vector indicating the rows (i.e., posterior samples) to be returned. If NULL (the default), all posterior samples are returned.

as.matrix

Should the output be a matrix instead of a data.frame? Defaults to FALSE.

as.array

Should the output be an array instead of a data.frame? Defaults to FALSE.

...

For as.data.frame, as.matrix, and as.array: Further arguments to be passed to posterior_samples.

row.names, optional

See as.data.frame.

Value

A data.frame (matrix or array) containing the posterior samples, with one column per parameter. In case an array is returned, it contains one additional dimension for the chains.

Details

Currently there are methods for brmsfit objects. as.data.frame.brmsfit, as.matrix.brmsfit, and as.array.brmsfit are basically aliases of posterior_samples.brmsfit and differ from each other only in type of the returned object.

Examples

# NOT RUN {
fit <- brm(rating ~ treat + period + carry + (1|subject), 
           data = inhaler, family = "cumulative")

# extract posterior samples of population-level effects 
samples1 <- posterior_samples(fit, "^b")
head(samples1)

# extract posterior samples of group-level standard deviations
samples2 <- posterior_samples(fit, "^sd_")
head(samples2)
# }
# NOT RUN {
# }

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