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(Deprecated) Extract Posterior Samples

Source: R/posterior_samples.R
posterior_samples.brmsfit.Rd

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.

See also

as_draws, as.data.frame

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 0.000302 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.02 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)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 5.969 seconds (Warm-up)
#> Chain 1:                4.268 seconds (Sampling)
#> Chain 1:                10.237 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000255 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.55 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 6.096 seconds (Warm-up)
#> Chain 2:                4.276 seconds (Sampling)
#> Chain 2:                10.372 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000258 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 2.58 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
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#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 6.167 seconds (Warm-up)
#> Chain 3:                4.266 seconds (Sampling)
#> Chain 3:                10.433 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000264 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 2.64 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 6.176 seconds (Warm-up)
#> Chain 4:                4.262 seconds (Sampling)
#> Chain 4:                10.438 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
#> 1      0.7606411       4.191504       5.431380 -1.4786572 -0.03591804
#> 2      0.5611575       3.636155       4.675608 -0.8359185  0.12082448
#> 3      0.7519345       3.896075       5.490320 -0.9635744  0.21765757
#> 4      0.5551249       3.642243       4.791505 -0.9486625  0.29633929
#> 5      0.5502091       3.998428       5.248111 -1.2993267  0.21801579
#> 6      0.7073057       3.666545       4.885156 -0.5402219  0.30515004
#>       b_carry
#> 1  0.06856922
#> 2 -0.41065894
#> 3 -0.27732837
#> 4 -0.29512434
#> 5 -0.24921882
#> 6 -0.33619624

# 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             1.3371472
#> 2             1.2369175
#> 3             1.2253835
#> 4             1.1883634
#> 5             1.3111031
#> 6             0.9595355
# }