(Deprecated) Extract Posterior Samples

Extract posterior samples of specified parameters. The posterior_samples method is deprecated. We recommend using the more modern and consistent as_draws_* extractor functions of the posterior package instead.

Usage

# S3 method for class 'brmsfit'
posterior_samples(
  x,
  pars = NA,
  fixed = FALSE,
  add_chain = FALSE,
  subset = NULL,
  as.matrix = FALSE,
  as.array = FALSE,
  ...
)

posterior_samples(x, pars = NA, ...)

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.
...: Arguments passed to individual methods (if applicable).

Value

A data.frame (matrix or array) containing the posterior samples.

Examples

# \dontrun{
fit <- brm(rating ~ treat + period + carry + (1|subject),
           data = inhaler, family = "cumulative")
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000258 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.58 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 5.736 seconds (Warm-up)
#> Chain 1:                4.023 seconds (Sampling)
#> Chain 1:                9.759 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000246 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 5.823 seconds (Warm-up)
#> Chain 2:                4.026 seconds (Sampling)
#> Chain 2:                9.849 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000249 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 2.49 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 5.75 seconds (Warm-up)
#> Chain 3:                4.043 seconds (Sampling)
#> Chain 3:                9.793 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000249 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 2.49 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 5.709 seconds (Warm-up)
#> Chain 4:                4.029 seconds (Sampling)
#> Chain 4:                9.738 seconds (Total)
#> Chain 4: 

# extract posterior samples of population-level effects
samples1 <- posterior_samples(fit, pars = "^b")
#> Warning: Method 'posterior_samples' is deprecated. Please see ?as_draws for recommended alternatives.
head(samples1)
#>   b_Intercept[1] b_Intercept[2] b_Intercept[3]    b_treat  b_period    b_carry
#> 1      0.8198606       3.939656       5.140543 -0.9602062 0.4819665 -0.4547403
#> 2      0.7909925       3.585550       5.719107 -0.6058937 0.3850753 -0.5284080
#> 3      0.4665873       4.265965       5.209711 -1.2353026 0.0429138 -0.1605685
#> 4      0.6634417       3.721982       5.405851 -0.9516327 0.3804130 -0.3182677
#> 5      1.0271149       4.549710       5.717646 -1.0292334 0.3223812 -0.2853782
#> 6      0.8868743       4.506237       5.517634 -1.2265462 0.4039052 -0.1269694

# extract posterior samples of group-level standard deviations
samples2 <- posterior_samples(fit, pars = "^sd_")
#> Warning: Method 'posterior_samples' is deprecated. Please see ?as_draws for recommended alternatives.
head(samples2)
#>   sd_subject__Intercept
#> 1              1.196121
#> 2              1.133190
#> 3              1.708739
#> 4              1.535925
#> 5              1.782294
#> 6              1.560718
# }

Usage

Arguments

Value

See also

Examples