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Extract Diagnostic Quantities of brms Models

Source: R/diagnostics.R
diagnostic-quantities.Rd

Extract quantities that can be used to diagnose sampling behavior of the algorithms applied by Stan at the back-end of brms.

Usage

# S3 method for class 'brmsfit'
log_posterior(object, ...)

# S3 method for class 'brmsfit'
nuts_params(object, pars = NULL, ...)

# S3 method for class 'brmsfit'
rhat(x, pars = NULL, ...)

# S3 method for class 'brmsfit'
neff_ratio(object, pars = NULL, ...)

Arguments

object, x

A brmsfit object.

...

Arguments passed to individual methods.

pars

An optional character vector of parameter names. For nuts_params these will be NUTS sampler parameter names rather than model parameters. If pars is omitted all parameters are included.

Value

The exact form of the output depends on the method.

Details

For more details see bayesplot-extractors.

Examples

# \dontrun{
fit <- brm(time ~ age * sex, data = kidney)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 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: 0.099 seconds (Warm-up)
#> Chain 1:                0.033 seconds (Sampling)
#> Chain 1:                0.132 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.04 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)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.117 seconds (Warm-up)
#> Chain 2:                0.042 seconds (Sampling)
#> Chain 2:                0.159 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.04 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)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.105 seconds (Warm-up)
#> Chain 3:                0.035 seconds (Sampling)
#> Chain 3:                0.14 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.108 seconds (Warm-up)
#> Chain 4:                0.034 seconds (Sampling)
#> Chain 4:                0.142 seconds (Total)
#> Chain 4: 

lp <- log_posterior(fit)
head(lp)
#>   Chain Iteration     Value
#> 1     1         1 -483.6940
#> 2     1         2 -483.4945
#> 3     1         3 -483.9464
#> 4     1         4 -482.8378
#> 5     1         5 -484.0765
#> 6     1         6 -484.2357

np <- nuts_params(fit)
str(np)
#> 'data.frame':	24000 obs. of  4 variables:
#>  $ Chain    : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ Iteration: int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ Parameter: Factor w/ 6 levels "accept_stat__",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Value    : num  0.999 0.955 0.964 0.997 0.93 ...
# extract the number of divergence transitions
sum(subset(np, Parameter == "divergent__")$Value)
#> [1] 0

head(rhat(fit))
#>     b_Intercept           b_age     b_sexfemale b_age:sexfemale           sigma 
#>        1.002666        1.002347        1.002364        1.002222        1.001104 
#>       Intercept 
#>        1.001395 
head(neff_ratio(fit))
#>     b_Intercept           b_age     b_sexfemale b_age:sexfemale           sigma 
#>       0.3625533       0.3462164       0.3499752       0.3245068       0.5271566 
#>       Intercept 
#>       0.5210288 
# }