R: Model Weighting Methods

model_weights.brmsfit {brms} | R Documentation

## 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)

[Package brms version 2.17.0 Index]