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Model-averaged predictions

Source: R/diagnostics_generic.R
model_average.Rd

Combine fitted values from several models using native model weights computed from the pointwise PSIS-LOO (or WAIC) elpd via tulpa_psis() – no loo package dependency. "loo" / "waic" give stacking weights (the simplex-optimal predictive combination); "pbma" / "pbma+" give pseudo-BMA(+) weights. Every model must carry an [n_draws x n_obs] pointwise log-likelihood (fit$draws$log_lik) over the same observations.

Usage

model_average(
  ...,
  weights = c("loo", "waic", "pbma", "pbma+"),
  fitted_fn = fitted
)

Arguments

...

Named tulpa_fit objects fitted to the same observations.

weights

"loo" (stacking, default), "waic", "pbma", or "pbma+".

fitted_fn

Function extracting a length-n_obs fitted vector from a fit (default fitted()).

Value

A list with averaged (the weighted fitted vector), weights (the named model weights), and comparison (the compare_models() table).

References

Yao, Vehtari, Simpson & Gelman (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis 13(3):917-1007.

See also

compare_models().

Examples

# \donttest{
set.seed(1)
df <- data.frame(x = rnorm(120))
df$y <- rpois(120, exp(0.4 + 0.5 * df$x))
f1 <- tulpa(y ~ x, data = df, family = "poisson", mode = "hmc",
            control = list(n_iter = 500L, warmup = 250L, seed = 1L))
f2 <- tulpa(y ~ 1, data = df, family = "poisson", mode = "hmc",
            control = list(n_iter = 500L, warmup = 250L, seed = 1L))
ma <- model_average(full = f1, null = f2, weights = "waic")
ma$weights
# }