`R/marginal-distributions.R`

`marginal_inference.Rd`

Creates marginal model-averaged and conditional posterior distributions based on a list of models, vector of parameters, formula, and a list of indicators of the null or alternative hypothesis models for each parameter. Computes inclusion Bayes factors for each marginal estimate via a Savage-Dickey density approximation.

```
marginal_inference(
model_list,
marginal_parameters,
parameters,
is_null_list,
formula,
null_hypothesis = 0,
normal_approximation = FALSE,
n_samples = 10000,
seed = NULL,
silent = FALSE
)
```

- model_list
list of models, each of which contains marginal likelihood estimated with bridge sampling

`marglik`

and prior model odds`prior_weights`

- marginal_parameters
parameters for which the the marginal summary should be created

- parameters
all parameters included in the model_list that are relevant for the formula (all of which need to have specification of

`is_null_list`

)- is_null_list
list with entries for each parameter carrying either logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

- formula
model formula (needs to be specified if

`parameter`

was part of a formula)- null_hypothesis
point null hypothesis to test. Defaults to

`0`

- normal_approximation
whether the height of prior and posterior density should be approximated via a normal distribution (rather than kernel density). Defaults to

`FALSE`

.- n_samples
number of samples to be drawn for the model-averaged posterior distribution

- seed
seed for random number generation

- silent
whether warnings should be returned silently. Defaults to

`FALSE`

`mix_posteriors`

returns a named list of mixed posterior
distributions (either a vector of matrix).