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Extract pointwise log-likelihood from a Stan model for a bellreg model

Source: R/loo.R
extract_log_lik.Rd

This function extracts the pointwise log-likelihood for a bellreg model.

Usage

extract_log_lik(object, ...)

Arguments

object

an object of the class bellreg.

...

further arguments passed to or from other methods.

Value

a matrix with the pointwise extracted log-likelihood associated with a bellreg model.

Examples

# \donttest{
data(faults)
fit <- bellreg(nf ~ lroll, data = faults, approach = "bayes")
#> 
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (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: 0.393 seconds (Warm-up)
#> Chain 1:                0.397 seconds (Sampling)
#> Chain 1:                0.79 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 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: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.393 seconds (Warm-up)
#> Chain 2:                0.392 seconds (Sampling)
#> Chain 2:                0.785 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.2e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.393 seconds (Warm-up)
#> Chain 3:                0.397 seconds (Sampling)
#> Chain 3:                0.79 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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)
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#> 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: 0.392 seconds (Warm-up)
#> Chain 4:                0.397 seconds (Sampling)
#> Chain 4:                0.789 seconds (Total)
#> Chain 4: 
loglik <- extract_log_lik(fit)

data(cells)
fit <- zibellreg(cells ~ 1|smoker+gender, data = cells, approach = "bayes", chains = 1, iter = 100)
#> 
#> SAMPLING FOR MODEL 'zibellreg' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000399 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.99 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 7
#> Chain 1:            adapt_window = 38
#> Chain 1:            term_buffer = 5
#> Chain 1: 
#> Chain 1: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 1: Iteration: 10 / 100 [ 10%]  (Warmup)
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#> Chain 1: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 1: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.177 seconds (Warm-up)
#> Chain 1:                0.194 seconds (Sampling)
#> Chain 1:                0.371 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.09, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
loglik <- extract_log_lik(fit)

# }