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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 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 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: 0.088 seconds (Warm-up)
#> Chain 1:                0.032 seconds (Sampling)
#> Chain 1:                0.12 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 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: 0.083 seconds (Warm-up)
#> Chain 2:                0.031 seconds (Sampling)
#> Chain 2:                0.114 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.02 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: 0.081 seconds (Warm-up)
#> Chain 3:                0.032 seconds (Sampling)
#> Chain 3:                0.113 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.02 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: 0.08 seconds (Warm-up)
#> Chain 4:                0.029 seconds (Sampling)
#> Chain 4:                0.109 seconds (Total)
#> Chain 4: 

lp <- log_posterior(fit)
head(lp)
#>   Chain Iteration     Value
#> 1     1         1 -485.2757
#> 2     1         2 -483.9077
#> 3     1         3 -487.3921
#> 4     1         4 -482.9472
#> 5     1         5 -483.5868
#> 6     1         6 -483.1267

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  0.698 0.997 0.539 0.927 0.997 ...
# 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 
#>        1.001802        1.001644        1.001714        1.001548        1.001708 
#>       Intercept 
#>        1.000732 
head(neff_ratio(fit))
#>     b_Intercept           b_age     b_sexfemale b_age:sexfemale           sigma 
#>       0.3660127       0.3501625       0.3324177       0.3125507       0.6367364 
#>       Intercept 
#>       0.6123605 
# }