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Model comparison with the loo package

Source: R/loo.R
loo_compare.brmsfit.Rd

For more details see loo_compare.

Usage

# S3 method for class 'brmsfit'
loo_compare(x, ..., criterion = c("loo", "waic", "kfold"), model_names = NULL)

Arguments

x

A brmsfit object.

...

More brmsfit objects.

criterion

The name of the criterion to be extracted from brmsfit objects.

model_names

If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

Value

An object of class "compare.loo".

Details

All brmsfit objects should contain precomputed criterion objects. See add_criterion for more help.

Examples

# \dontrun{
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
            data = inhaler)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 9e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.026 seconds (Warm-up)
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#> Chain 1:                0.051 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 7e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 6e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 5e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
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#> Chain 4:  Elapsed Time: 0.026 seconds (Warm-up)
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#> Chain 4: 
fit1 <- add_criterion(fit1, "waic")
#> Warning: 
#> 2 (0.3%) p_waic estimates greater than 0.4. We recommend trying loo instead.

# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
            data = inhaler)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1:  Elapsed Time: 1.235 seconds (Warm-up)
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
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fit2 <- add_criterion(fit2, "waic")
#> Warning: 
#> 26 (4.5%) p_waic estimates greater than 0.4. We recommend trying loo instead.

# compare both models
loo_compare(fit1, fit2, criterion = "waic")
#>  model elpd_diff se_diff p_worse diag_diff diag_elpd
#>   fit2       0.0     0.0      NA                    
#>   fit1      -9.4     4.4    0.98                    
# }