# inclusion: Inclusion Bayes Factor In metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

## Description

Computes the inclusion Bayes factor for two sets of models (e.g., A={M1,M2} vs. B={M3,M4}).

## Usage

 `1` ```inclusion(logml, include = 1, prior = 1) ```

## Arguments

 `logml` a vector with log-marginal likelihoods. Alternatively, a list with meta-analysis models (fitted via `meta_random` or `meta_fixed`). `include` integer vector which models to include in inclusion Bayes factor/posterior probability. If only two marginal likelihoods/meta-analyses are supplied, the inclusion Bayes factor is identical to the usual Bayes factor BF_{M1,M2}. One can include models depending on the names of the models (such as `"random_H1"`) by providing a character value, for instance: `include="H1"` (all H1 vs. all H0 models) or `include="random"` (all random- vs. all fixed-effects models). `prior` prior probabilities over models (possibly unnormalized). For instance, if the first model is as likely as models 2, 3 and 4 together: `prior = c(3,1,1,1)`. The default is a discrete uniform distribution over models.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```#### Example with simple Normal-distribution models # generate data: x <- rnorm(50) # Model 1: x ~ Normal(0,1) logm1 <- sum(dnorm(x, log = TRUE)) # Model 2: x ~ Normal(.2, 1) logm2 <- sum(dnorm(x, mean = .2, log = TRUE)) # Model 3: x ~ Student-t(df=2) logm3 <- sum(dt(x, df = 2, log = TRUE)) # BF: Correct (Model 1) vs. misspecified (2 & 3) inclusion(c(logm1, logm2, logm3), include = 1) ```

### Example output

```Loading required package: Rcpp
Warning message:
In file(con, "r") : cannot open file '/proc/stat': Permission denied
### Inclusion Bayes factor ###
Model Prior Posterior included
1 Model 1 0.333   0.98326        x
2 Model 2 0.333   0.01497
3 Model 3 0.333   0.00177

Inclusion posterior probability: 0.983
Inclusion Bayes factor: 117.507
```

metaBMA documentation built on March 17, 2021, 9:06 a.m.