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Compare Information Criteria of Different Models

Source: R/loo.R
compare_ic.Rd

Compare information criteria of different models fitted with waic or loo. Deprecated and will be removed in the future. Please use loo_compare instead.

Usage

compare_ic(..., x = NULL, ic = c("loo", "waic", "kfold"))

Arguments

...

At least two objects returned by waic or loo. Alternatively, brmsfit objects with information criteria precomputed via add_ic may be passed, as well.

x

A list containing the same types of objects as can be passed via ....

ic

The name of the information criterion to be extracted from brmsfit objects. Ignored if information criterion objects are only passed directly.

Value

An object of class iclist.

Details

See loo_compare for the recommended way of comparing models with the loo package.

See also

loo, loo_compare add_criterion

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: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.035 seconds (Warm-up)
#> Chain 1:                0.032 seconds (Sampling)
#> Chain 1:                0.067 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 5e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.033 seconds (Warm-up)
#> Chain 2:                0.029 seconds (Sampling)
#> Chain 2:                0.062 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 5e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.034 seconds (Warm-up)
#> Chain 3:                0.031 seconds (Sampling)
#> Chain 3:                0.065 seconds (Total)
#> 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.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.034 seconds (Warm-up)
#> Chain 4:                0.029 seconds (Sampling)
#> Chain 4:                0.063 seconds (Total)
#> Chain 4: 
waic1 <- waic(fit1)
#> 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 0.000648 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.48 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: 1.535 seconds (Warm-up)
#> Chain 1:                0.763 seconds (Sampling)
#> Chain 1:                2.298 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 1.576 seconds (Warm-up)
#> Chain 2:                0.761 seconds (Sampling)
#> Chain 2:                2.337 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 1.651 seconds (Warm-up)
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#> Chain 3:                3.128 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 1.583 seconds (Warm-up)
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#> Chain 4:                2.352 seconds (Total)
#> Chain 4: 
waic2 <- waic(fit2)
#> Warning: 
#> 26 (4.5%) p_waic estimates greater than 0.4. We recommend trying loo instead.

# compare both models
compare_ic(waic1, waic2)
#> Warning: 'compare_ic' is deprecated and will be removed in the future. Please use 'loo_compare' instead.
#>                WAIC    SE
#> fit1        1058.79 51.87
#> fit2        1039.46 52.20
#> fit1 - fit2   19.33  8.92
# }