brms: model_weights.brmsfit – R documentation

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model_weights.brmsfit

Model Weighting Methods

Description

Compute model weights in various ways, for instance, via stacking of posterior predictive distributions, Akaike weights, or marginal likelihoods.

Usage

## S3 method for class 'brmsfit'
model_weights(x, ..., weights = "stacking", model_names = NULL)

model_weights(x, ...)

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.
`weights`	Name of the criterion to compute weights from. Should be one of `"loo"`, `"waic"`, `"kfold"`, `"stacking"` (current default), or `"bma"`, `"pseudobma"`, For the former three options, Akaike weights will be computed based on the information criterion values returned by the respective methods. For `"stacking"` and `"pseudobma"`, method `loo_model_weights` will be used to obtain weights. For `"bma"`, method `post_prob` will be used to compute Bayesian model averaging weights based on log marginal likelihood values (make sure to specify reasonable priors in this case). For some methods, `weights` may also be a numeric vector of pre-specified weights.
`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 numeric vector of weights for the models.

Examples

## Not run: 
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
summary(fit1)

# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)
summary(fit2)

# obtain Akaike weights based on the WAIC
model_weights(fit1, fit2, weights = "waic")

## End(Not run)

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10