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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 1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.1 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: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.035 seconds (Warm-up)
#> Chain 1:                0.036 seconds (Sampling)
#> Chain 1:                0.071 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 6e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.035 seconds (Warm-up)
#> Chain 2:                0.032 seconds (Sampling)
#> Chain 2:                0.067 seconds (Total)
#> 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.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.036 seconds (Warm-up)
#> Chain 3:                0.035 seconds (Sampling)
#> Chain 3:                0.071 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 6e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.06 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: 0.036 seconds (Warm-up)
#> Chain 4:                0.032 seconds (Sampling)
#> Chain 4:                0.068 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 0.000635 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.35 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (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: 1.513 seconds (Warm-up)
#> Chain 1:                0.746 seconds (Sampling)
#> Chain 1:                2.259 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 1.528 seconds (Warm-up)
#> Chain 2:                0.739 seconds (Sampling)
#> Chain 2:                2.267 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 1.612 seconds (Warm-up)
#> Chain 3:                1.427 seconds (Sampling)
#> Chain 3:                3.039 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 6.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 1.531 seconds (Warm-up)
#> Chain 4:                0.737 seconds (Sampling)
#> Chain 4:                2.268 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
# }