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Extract diagnostic quantities of mvgam models

Source: R/mvgam_diagnostics.R
mvgam_diagnostics.Rd

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

Usage

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

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

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

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

Arguments

object, x

A mvgam or jsdgam object.

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.

...

Arguments passed to individual methods.

Value

The exact form of the output depends on the method.

Details

For more details see bayesplot-extractors.

Examples

# \donttest{
simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
            trend_model = AR(),
            noncentred = TRUE,
            data = simdat$data_train,
            chains = 2)
#> Compiling Stan program using cmdstanr
#> 
#> Start sampling
#> Running MCMC with 2 parallel chains...
#> 
#> Chain 1 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 1 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 1 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 1 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 2 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 2 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 2 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 1 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 1 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 1 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 2 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 2 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 2 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 2 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 2 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 2 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 1 finished in 0.3 seconds.
#> Chain 2 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 2 finished in 0.3 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 0.3 seconds.
#> Total execution time: 0.5 seconds.
#> 
np <- nuts_params(mod)
head(np)
#>   Chain Iteration     Parameter    Value
#> 1     1         1 accept_stat__ 1.000000
#> 2     1         2 accept_stat__ 0.992268
#> 3     1         3 accept_stat__ 0.777018
#> 4     1         4 accept_stat__ 0.989895
#> 5     1         5 accept_stat__ 0.854329
#> 6     1         6 accept_stat__ 0.928752

# extract the number of divergence transitions
sum(subset(np, Parameter == "divergent__")$Value)
#> [1] 0

head(neff_ratio(mod))
#>  mus[1,1]  mus[2,1]  mus[3,1]  mus[4,1]  mus[5,1]  mus[6,1] 
#> 0.7872905 0.6691465 0.7611481 0.5512319 0.7750102 0.7717447 
# }