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Log marginal likelihood calculation

Source: R/zzz-methods-bayes-factor.R
log_mll.Rd

Calculate the log marginal likelihood with bridge sampling (Meng & Wong, 1996). This is a wrapper around bridgesampling::bridge_sampler(). Therefore, log marginal likelihood calculation is currently only available for models estimated with {rstan} using MCMC.

Usage

log_mll(x, ..., force = FALSE)

Arguments

x

A measrdcm object estimated with backend = "optim".

...

Unused.

force

If the criterion has already been added to the model object with add_criterion(), should it be recalculated. Default is FALSE.

Value

The estimate of the log marginal likelihood.

References

Meng, X.-L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistical Sinica, 6(4), 831-860. https://www.jstor.org/stable/24306045

Examples

model_spec <- dcm_specify(qmatrix = dcmdata::mdm_qmatrix,
                          identifier = "item")
model <- dcm_estimate(dcm_spec = model_spec, data = dcmdata::mdm_data,
                      identifier = "respondent", method = "mcmc",
                      seed = 63277)
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.00013 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.3 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 7.445 seconds (Warm-up)
#> Chain 1:                7.333 seconds (Sampling)
#> Chain 1:                14.778 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000129 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.29 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 4000 [  0%]  (Warmup)
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#> Chain 2: Iteration: 4000 / 4000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 7.618 seconds (Warm-up)
#> Chain 2:                9.113 seconds (Sampling)
#> Chain 2:                16.731 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000125 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.25 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
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#> Chain 3: Iteration: 4000 / 4000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 7.494 seconds (Warm-up)
#> Chain 3:                6.204 seconds (Sampling)
#> Chain 3:                13.698 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000125 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.25 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 4000 [  0%]  (Warmup)
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#> Chain 4: Iteration: 3600 / 4000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 4000 / 4000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 7.772 seconds (Warm-up)
#> Chain 4:                7.211 seconds (Sampling)
#> Chain 4:                14.983 seconds (Total)
#> Chain 4: 
#> Warning: There were 2 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems

log_mll(model)
#> [1] -346.3355