Model averaging/weighting via stacking or pseudo-BMA weighting

Model averaging via stacking of predictive distributions, pseudo-BMA weighting or pseudo-BMA+ weighting with the Bayesian bootstrap. See Yao et al. (2018), Vehtari, Gelman, and Gabry (2017), and Vehtari, Simpson, Gelman, Yao, and Gabry (2024) for background.

Usage

loo_model_weights(x, ...)

# Default S3 method
loo_model_weights(
  x,
  ...,
  method = c("stacking", "pseudobma"),
  optim_method = "BFGS",
  optim_control = list(),
  BB = TRUE,
  BB_n = 1000,
  alpha = 1,
  r_eff_list = NULL,
  cores = getOption("mc.cores", 1)
)

stacking_weights(lpd_point, optim_method = "BFGS", optim_control = list())

pseudobma_weights(lpd_point, BB = TRUE, BB_n = 1000, alpha = 1)

Arguments

x

A list of "psis_loo" objects (objects returned by loo()) or pointwise log-likelihood matrices, one for each model. If the list elements are named the names will be used to label the models in the results. Each matrix/object should have dimensions \(S\) by \(N\), where \(S\) is the size of the posterior sample (with all chains merged) and \(N\) is the number of data points. All models must have the same \(N\), but \(S\) can differ between models. If x is a list of log-likelihood matrices then loo() is called internally on each matrix. Currently the loo_model_weights() function is not implemented to be used with results from K-fold CV, but you can still obtain weights using K-fold CV results by calling the stacking_weights() or pseudobma_weights() function directly. Objects from loo_subsample() are supported only with method = "pseudobma" and BB = FALSE, which use the subsampling estimates of elpd_loo. The weights do not account for the subsampling uncertainty of these estimates. The subsamples can differ between the models, but the differences between the estimates are more accurate when all models use the same observations (pass the first object as the observations argument of loo_subsample() for the other models). Stacking and the Bayesian bootstrap need pointwise values for all observations, and objects from loo_subsample() have them only for the subsampled observations.

...

Unused, except for the generic to pass arguments to individual methods.

method

Either "stacking" (the default) or "pseudobma", indicating which method to use for obtaining the weights. "stacking" refers to stacking of predictive distributions and "pseudobma" refers to pseudo-BMA+ weighting (or plain pseudo-BMA weighting if argument BB is FALSE).

optim_method

If method="stacking", a string passed to the method argument of stats::constrOptim() to specify the optimization algorithm. The default is optim_method="BFGS", but other options are available (see stats::optim()).

optim_control

If method="stacking", a list of control parameters for optimization passed to the control argument of stats::constrOptim().

BB

Logical used when "method"="pseudobma". If TRUE (the default), the Bayesian bootstrap will be used to adjust the pseudo-BMA weighting, which is called pseudo-BMA+ weighting. It helps regularize the weight away from 0 and 1, so as to reduce the variance.

BB_n

For pseudo-BMA+ weighting only, the number of samples to use for the Bayesian bootstrap. The default is BB_n=1000.

alpha

Positive scalar shape parameter in the Dirichlet distribution used for the Bayesian bootstrap. The default is alpha=1, which corresponds to a uniform distribution on the simplex space.

r_eff_list

Optionally, a list of relative effective sample size estimates for the likelihood (exp(log_lik)) of each observation in each model. See psis() and relative_eff() helper function for computing r_eff. If x is a list of "psis_loo" objects then r_eff_list is ignored.

cores

The number of cores to use for parallelization. This defaults to the option mc.cores which can be set for an entire R session by options(mc.cores = NUMBER). The old option loo.cores is now deprecated but will be given precedence over mc.cores until loo.cores is removed in a future release. As of version 2.0.0 the default is now 1 core if mc.cores is not set, but we recommend using as many (or close to as many) cores as possible.

• Note for Windows 10 users: it is strongly recommended to avoid using the .Rprofile file to set mc.cores (using the cores argument or setting mc.cores interactively or in a script is fine).

lpd_point

If calling stacking_weights() or pseudobma_weights() directly, a matrix of pointwise leave-one-out (or K-fold) log likelihoods evaluated for different models. It should be a \(N\) by \(K\) matrix where \(N\) is sample size and \(K\) is the number of models. Each column corresponds to one model. These values can be calculated approximately using loo() or by running exact leave-one-out or K-fold cross-validation.

Value

A numeric vector containing one weight for each model.

Details

loo_model_weights() is a wrapper around the stacking_weights() and pseudobma_weights() functions that implements stacking, pseudo-BMA, and pseudo-BMA+ weighting for combining multiple predictive distributions. We can use approximate or exact leave-one-out cross-validation (LOO-CV) or K-fold CV to estimate the expected log predictive density (ELPD).

The stacking method (method="stacking"), which is the default for loo_model_weights(), combines all models by maximizing the leave-one-out predictive density of the combination distribution. That is, it finds the optimal linear combining weights for maximizing the leave-one-out log score.

The pseudo-BMA method (method="pseudobma") finds the relative weights proportional to the ELPD of each model. However, when method="pseudobma", the default is to also use the Bayesian bootstrap (BB=TRUE), which corresponds to the pseudo-BMA+ method. The Bayesian bootstrap takes into account the uncertainty of finite data points and regularizes the weights away from the extremes of 0 and 1.

In general, we recommend stacking for averaging predictive distributions, while pseudo-BMA+ can serve as a computationally easier alternative.

References

Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4 (journal version, preprint arXiv:1507.04544).

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58. PDF

Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) Using stacking to average Bayesian predictive distributions. Bayesian Analysis, advance publication, doi:10.1214/17-BA1091. (online).

See also

• The loo package vignettes, particularly Bayesian Stacking and Pseudo-BMA weights using the loo package.

• loo() for details on leave-one-out ELPD estimation.

• constrOptim() for the choice of optimization methods and control-parameters.

• relative_eff() for computing r_eff.

Examples

# \dontrun{
### Demonstrating usage after fitting models with RStan
library(rstan)
#> Loading required package: StanHeaders
#> 
#> rstan version 2.32.7 (Stan version 2.39.0)
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores()).
#> To avoid recompilation of unchanged Stan programs, we recommend calling
#> rstan_options(auto_write = TRUE)
#> For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
#> change `threads_per_chain` option:
#> rstan_options(threads_per_chain = 1)

# generate fake data from N(0,1).
N <- 100
y <- rnorm(N, 0, 1)

# Suppose we have three models: N(-1, sigma), N(0.5, sigma) and N(0.6,sigma).
stan_code <- "
  data {
    int N;
    vector[N] y;
    real mu_fixed;
  }
  parameters {
    real<lower=0> sigma;
  }
  model {
    sigma ~ exponential(1);
    y ~ normal(mu_fixed, sigma);
  }
  generated quantities {
    vector[N] log_lik;
    for (n in 1:N) log_lik[n] = normal_lpdf(y[n]| mu_fixed, sigma);
  }"

mod <- stan_model(model_code = stan_code)
fit1 <- sampling(mod, data=list(N=N, y=y, mu_fixed=-1))
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 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)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 1:                0.005 seconds (Sampling)
#> Chain 1:                0.01 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 2:                0.005 seconds (Sampling)
#> Chain 2:                0.01 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 3:                0.005 seconds (Sampling)
#> Chain 3:                0.01 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.006 seconds (Warm-up)
#> Chain 4:                0.005 seconds (Sampling)
#> Chain 4:                0.011 seconds (Total)
#> Chain 4: 
fit2 <- sampling(mod, data=list(N=N, y=y, mu_fixed=0.5))
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.04 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)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.006 seconds (Warm-up)
#> Chain 1:                0.005 seconds (Sampling)
#> Chain 1:                0.011 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 2:                0.005 seconds (Sampling)
#> Chain 2:                0.01 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.006 seconds (Warm-up)
#> Chain 3:                0.005 seconds (Sampling)
#> Chain 3:                0.011 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 4:                0.005 seconds (Sampling)
#> Chain 4:                0.01 seconds (Total)
#> Chain 4: 
fit3 <- sampling(mod, data=list(N=N, y=y, mu_fixed=0.6))
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.04 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)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 1:                0.005 seconds (Sampling)
#> Chain 1:                0.01 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 2:                0.005 seconds (Sampling)
#> Chain 2:                0.01 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 3:                0.005 seconds (Sampling)
#> Chain 3:                0.01 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 4:                0.005 seconds (Sampling)
#> Chain 4:                0.01 seconds (Total)
#> Chain 4: 
model_list <- list(fit1, fit2, fit3)
log_lik_list <- lapply(model_list, extract_log_lik)

# optional but recommended
r_eff_list <- lapply(model_list, function(x) {
  ll_array <- extract_log_lik(x, merge_chains = FALSE)
  relative_eff(exp(ll_array))
})

# stacking method:
wts1 <- loo_model_weights(
  log_lik_list,
  method = "stacking",
  r_eff_list = r_eff_list,
  optim_control = list(reltol=1e-10)
)
print(wts1)
#> Method: stacking
#> ------
#>        weight
#> model1 0.243 
#> model2 0.757 
#> model3 0.000 

# can also pass a list of psis_loo objects to avoid recomputing loo
loo_list <- lapply(1:length(log_lik_list), function(j) {
  loo(log_lik_list[[j]], r_eff = r_eff_list[[j]])
})

wts2 <- loo_model_weights(
  loo_list,
  method = "stacking",
  optim_control = list(reltol=1e-10)
)
all.equal(wts1, wts2)
#> [1] TRUE

# can provide names to be used in the results
loo_model_weights(setNames(loo_list, c("A", "B", "C")))
#> Method: stacking
#> ------
#>   weight
#> A 0.243 
#> B 0.757 
#> C 0.000 


# pseudo-BMA+ method:
set.seed(1414)
loo_model_weights(loo_list, method = "pseudobma")
#> Method: pseudo-BMA+ with Bayesian bootstrap
#> ------
#>        weight
#> model1 0.035 
#> model2 0.951 
#> model3 0.014 

# pseudo-BMA method (set BB = FALSE):
loo_model_weights(loo_list, method = "pseudobma", BB = FALSE)
#> Method: pseudo-BMA
#> ------
#>        weight
#> model1 0.000 
#> model2 0.988 
#> model3 0.012 

# calling stacking_weights or pseudobma_weights directly
lpd1 <- loo(log_lik_list[[1]], r_eff = r_eff_list[[1]])$pointwise[,1]
lpd2 <- loo(log_lik_list[[2]], r_eff = r_eff_list[[2]])$pointwise[,1]
lpd3 <- loo(log_lik_list[[3]], r_eff = r_eff_list[[3]])$pointwise[,1]
stacking_weights(cbind(lpd1, lpd2, lpd3))
#> Method: stacking
#> ------
#>        weight
#> model1 0.243 
#> model2 0.757 
#> model3 0.000 
pseudobma_weights(cbind(lpd1, lpd2, lpd3))
#> Method: pseudo-BMA+ with Bayesian bootstrap
#> ------
#>        weight
#> model1 0.038 
#> model2 0.948 
#> model3 0.015 
pseudobma_weights(cbind(lpd1, lpd2, lpd3), BB = FALSE)
#> Method: pseudo-BMA
#> ------
#>        weight
#> model1 0.000 
#> model2 0.988 
#> model3 0.012 
# }