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Model averaging via stacking or pseudo-BMA weighting.

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

Compute model weights for brmsfit objects via stacking or pseudo-BMA weighting. For more details, see loo::loo_model_weights.

Usage

# S3 method for class 'brmsfit'
loo_model_weights(x, ..., model_names = NULL)

Arguments

x

A brmsfit object.

...

More brmsfit objects or further arguments passed to the underlying post-processing functions. In particular, see prepare_predictions for further supported arguments.

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

A named vector of model weights.

Examples

# \dontrun{
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
            data = inhaler, family = "gaussian")
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.024 seconds (Warm-up)
#> Chain 1:                0.023 seconds (Sampling)
#> Chain 1:                0.047 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.024 seconds (Warm-up)
#> Chain 2:                0.022 seconds (Sampling)
#> Chain 2:                0.046 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
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#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.023 seconds (Warm-up)
#> Chain 3:                0.02 seconds (Sampling)
#> Chain 3:                0.043 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 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: 0.024 seconds (Warm-up)
#> Chain 4:                0.021 seconds (Sampling)
#> Chain 4:                0.045 seconds (Total)
#> Chain 4: 
# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
            data = inhaler, family = "gaussian")
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.51 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: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 1.473 seconds (Warm-up)
#> Chain 1:                0.658 seconds (Sampling)
#> Chain 1:                2.131 seconds (Total)
#> 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.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 1.555 seconds (Warm-up)
#> Chain 2:                1.299 seconds (Sampling)
#> Chain 2:                2.854 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 3.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 1.433 seconds (Warm-up)
#> Chain 3:                0.686 seconds (Sampling)
#> Chain 3:                2.119 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 1.362 seconds (Warm-up)
#> Chain 4:                0.658 seconds (Sampling)
#> Chain 4:                2.02 seconds (Total)
#> Chain 4: 
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
loo_model_weights(fit1, fit2)
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> Method: stacking
#> ------
#>      weight
#> fit1 0.145 
#> fit2 0.855 
# }