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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 0.000255 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.55 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: 4.181 seconds (Warm-up)
#> Chain 1:                2.965 seconds (Sampling)
#> Chain 1:                7.146 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000188 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.88 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: 1001 / 2000 [ 50%]  (Sampling)
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#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 4.141 seconds (Warm-up)
#> Chain 2:                2.962 seconds (Sampling)
#> Chain 2:                7.103 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000187 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.87 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (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: 4.245 seconds (Warm-up)
#> Chain 3:                2.96 seconds (Sampling)
#> Chain 3:                7.205 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000179 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.79 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: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 4.224 seconds (Warm-up)
#> Chain 4:                2.97 seconds (Sampling)
#> Chain 4:                7.194 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      0.6342171       3.407759       4.924798 -1.2274213  0.238568168
#> 2      0.7047514       4.184251       5.218924 -0.9563724  0.269829086
#> 3      0.6502745       3.722971       5.410921 -0.5231338  0.268173971
#> 4      0.7485675       4.255780       5.103656 -1.3328422  0.451319378
#> 5      0.7370135       3.558930       5.094945 -0.9954435 -0.046348482
#> 6      0.7413192       3.965001       5.474634 -1.0588954 -0.008753989
#>       b_carry
#> 1  0.15806054
#> 2 -0.05294319
#> 3 -0.51239519
#> 4  0.10237665
#> 5 -0.36615368
#> 6 -0.25920380

# 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             0.9596501
#> 2             1.4156071
#> 3             1.2941843
#> 4             1.2327454
#> 5             1.3554111
#> 6             1.3621803
# }