By default, the package estimates an ensemble of 36 meta-analytic models and provides functions for convenient manipulation with the fitted object. However, it has been built in a way that it can be used as a framework for estimating any combination of meta-analytic models (or a single model). Here, we illustrate how to build a custom ensemble of meta-analytic models - specifically the same ensemble that is used in ‘classical’ Bayesian Model-Averaged Meta-Analysis (Bartoš et al., 2021; Gronau et al., 2017, 2021). See this vignette if you are interested in building more customized ensembles or Bartoš et al. (in press) for a tutorial on fitting (custom) models in JASP.

We illustrate how to fit a classical BMA (not adjusting for
publication bias) using `RoBMA`

. For this purpose, we
reproduce a meta-analysis of registered reports on Power posing by Gronau et al. (2017). We focus only on the
analysis using all reported results using a Cauchy prior distribution
with scale \(1/\sqrt{2}\) for the
effect size estimation (half-Cauchy for testing) and inverse-gamma
distribution with scale = 1 and shape 0.15 for the heterogeneity
parameter. You can find the figure from the original publication here
and the paper’s supplementary materials at https://osf.io/fxg32/.

First, we load the power posing data provided within the metaBMA package and reproduce the analysis performed by Gronau et al. (2017).

```
data("power_pose", package = "metaBMA")
c("study", "effectSize", "SE")]
power_pose[,#> study effectSize SE
#> 1 Bailey et al. 0.2507640 0.2071399
#> 2 Ronay et al. 0.2275180 0.1931046
#> 3 Klaschinski et al. 0.3186069 0.1423228
#> 4 Bombari et al. 0.2832082 0.1421356
#> 5 Latu et al. 0.1463949 0.1416107
#> 6 Keller et al. 0.1509773 0.1221166
```

```
<- metaBMA::meta_bma(y = power_pose$effectSize, SE = power_pose$SE,
fit_BMA_test d = metaBMA::prior(family = "halfcauchy", param = 1/sqrt(2)),
tau = metaBMA::prior(family = "invgamma", param = c(1, .15)))
<- metaBMA::meta_bma(y = power_pose$effectSize, SE = power_pose$SE,
fit_BMA_est d = metaBMA::prior(family = "cauchy", param = c(0, 1/sqrt(2))),
tau = metaBMA::prior(family = "invgamma", param = c(1, .15)))
```

```
$inclusion
fit_BMA_test#> ### Inclusion Bayes factor ###
#> Model Prior Posterior included
#> 1 fixed_H0 0.25 0.00868
#> 2 fixed_H1 0.25 0.77745 x
#> 3 random_H0 0.25 0.02061
#> 4 random_H1 0.25 0.19325 x
#>
#> Inclusion posterior probability: 0.971
#> Inclusion Bayes factor: 33.136
round(fit_BMA_est$estimates,2)
#> mean sd 2.5% 50% 97.5% hpd95_lower hpd95_upper n_eff Rhat
#> averaged 0.22 0.06 0.09 0.22 0.34 0.09 0.34 NA NA
#> fixed 0.22 0.06 0.10 0.22 0.34 0.10 0.34 3026.5 1
#> random 0.22 0.08 0.07 0.22 0.37 0.07 0.37 6600.4 1
```

From the output, we can see that the inclusion Bayes factor for the
effect size was \(BF_{10} = 33.14\) and
the effect size estimate 0.22, 95% HDI [0.09, 0.34] which matches the
reported results. Please note that the `metaBMA`

package
model-averages only across the \(H_{1}\) models, whereas the
`RoBMA`

package model-averages across all models.

Now we reproduce the analysis with `RoBMA`

. We set the
corresponding prior distributions for effect sizes (\(\mu\)) and heterogeneity (\(\tau\)), and remove the alternative prior
distributions for the publication bias by setting
`priors_bias = NULL`

. To specify the half-Cauchy prior
distribution with the `RoBMA::prior()`

function we use a
regular Cauchy distribution and truncate it at zero (note that both
`metaBMA`

and `RoBMA`

export their own
`prior()`

functions that will clash when loading both
packages simultaneously). The inverse-gamma prior distribution for the
heterogeneity parameter is the default option (we specify it for
completeness) and we omit the specifications for the null prior
distributions for the effect size, heterogeneity (both of which are set
to a spike at 0 by default), and publication bias (which is set to no
publication bias by default).

Since `metaBMA`

model-averages the effect size estimates
only across the models assuming presence of the effect, we remove the
models assuming absence of the effect from the estimation ensemble with
`priors_effect_null = NULL`

. Finally, we set
`transformation = "cohens_d"`

to estimate the models on
Cohen’s *d* scale (RoBMA uses Fisher’s *z* scale by
default and transforms the estimated coefficients back to the scale that
us used for specifying the prior distributions), we speed the
computation by setting `parallel = TRUE`

, and set a seed for
reproducibility.

```
library(RoBMA)
<- RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study,
fit_RoBMA_test priors_effect = prior(
distribution = "cauchy",
parameters = list(location = 0, scale = 1/sqrt(2)),
truncation = list(0, Inf)),
priors_heterogeneity = prior(
distribution = "invgamma",
parameters = list(shape = 1, scale = 0.15)),
priors_bias = NULL,
transformation = "cohens_d", seed = 1, parallel = TRUE)
<- RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study,
fit_RoBMA_est priors_effect = prior(
distribution = "cauchy",
parameters = list(location = 0, scale = 1/sqrt(2))),
priors_heterogeneity = prior(
distribution = "invgamma",
parameters = list(shape = 1, scale = 0.15)),
priors_bias = NULL,
priors_effect_null = NULL,
transformation = "cohens_d", seed = 2, parallel = TRUE)
```

```
summary(fit_RoBMA_test)
#> Call:
#> RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study,
#> transformation = "cohens_d", priors_effect = prior(distribution = "cauchy",
#> parameters = list(location = 0, scale = 1/sqrt(2)), truncation = list(0,
#> Inf)), priors_heterogeneity = prior(distribution = "invgamma",
#> parameters = list(shape = 1, scale = 0.15)), priors_bias = NULL,
#> parallel = TRUE, seed = 1)
#>
#> Robust Bayesian meta-analysis
#> Components summary:
#> Models Prior prob. Post. prob. Inclusion BF
#> Effect 2/4 0.500 0.971 33.112
#> Heterogeneity 2/4 0.500 0.214 0.273
#> Bias 0/4 0.000 0.000 0.000
#>
#> Model-averaged estimates:
#> Mean Median 0.025 0.975
#> mu 0.213 0.217 0.000 0.348
#> tau 0.022 0.000 0.000 0.178
#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
summary(fit_RoBMA_est)
#> Call:
#> RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study,
#> transformation = "cohens_d", priors_effect = prior(distribution = "cauchy",
#> parameters = list(location = 0, scale = 1/sqrt(2))),
#> priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
#> scale = 0.15)), priors_bias = NULL, priors_effect_null = NULL,
#> parallel = TRUE, seed = 2)
#>
#> Robust Bayesian meta-analysis
#> Components summary:
#> Models Prior prob. Post. prob. Inclusion BF
#> Effect 2/2 1.000 1.000 Inf
#> Heterogeneity 1/2 0.500 0.200 0.250
#> Bias 0/2 0.000 0.000 0.000
#>
#> Model-averaged estimates:
#> Mean Median 0.025 0.975
#> mu 0.220 0.220 0.096 0.346
#> tau 0.019 0.000 0.000 0.152
#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
```

The output from the `summary.RoBMA()`

function has 2
parts. The first one under the “Robust Bayesian Meta-Analysis” heading
provides a basic summary of the fitted models by component types
(presence of the Effect/Heterogeneity/Publication bias). We can see that
there are no models correcting for publication bias (we disabled them by
setting `priors_bias = NULL`

). Furthermore, the table
summarizes the prior and posterior probabilities and the inclusion Bayes
factors of the individual components. The results for the half-Cauchy
model specified for testing show that the inclusion BF is basically
identical to the one computed by the `metaBMA`

package, \(\text{BF}_{10} = 33.11\).

The second part under the ‘Model-averaged estimates’ heading displays
the parameter estimates. The results for the unrestricted Cauchy model
specified for estimation show the effect size estimate \(\mu = 0.22\), 95% CI [0.10, 0.35] that also
mirrors the one obtained from `metaBMA`

package.

RoBMA provides extensive options for visualizing the results. Here, we visualize the prior (grey) and posterior (black) distribution for the mean parameter.

`plot(fit_RoBMA_est, parameter = "mu", prior = TRUE, xlim = c(-1, 1))`

If we visualize the effect size from the model specified for testing,
we notice a few more things. The function plots the model-averaged
estimates across all models by default (here model-averaged across
models assuming the absence of the effect). The arrows stand for the
probability of a spike, here, at the value 0. The secondary y-axis
(right) shows the probability of the value 0 decreased from .50, to 0.03
(also obtainable from the “Robust Bayesian Meta-Analysis” field in the
`summary.RoBMA()`

function). Furthermore, the continuous
prior distributions for the effect size under the alternative hypothesis
is truncated to only the positive numbers, as specified when fitting the
models.

`plot(fit_RoBMA_test, parameter = "mu", prior = TRUE, xlim = c(-.5, 1))`

We can also visualize the estimates from the individual models used
in the ensemble. We do that with the `plot_models()`

which
visualizes the effect size estimates and 95% CI of each of the specified
models from the estimation ensemble (Model 1 corresponds to the fixed
effect model and Model 2 to the random effect model). The size of the
square representing the mean estimate reflects the posterior model
probability of the model, which is also displayed in the right-hand side
panel. The bottom part of the figure shows the model averaged-estimate
that is a combination of the individual model posterior distributions
weighted by the posterior model probabilities.

`plot_models(fit_RoBMA_est)`

The last type of visualization that we show here is the forest plot.
It displays the original studies’ effects and the meta-analytic estimate
within one figure. It can be requested by using the
`forest()`

function.

`forest(fit_RoBMA_est)`

For more options provided by the plotting function, see its
documentation using `?plot.RoBMA()`

,
`?plot_models()`

, and `?forest()`

.

Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., &
Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in
medicine. *Statistics in Medicine*. https://doi.org/10.1002/sim.9170

Bartoš, F., Maier, M., Quintana, D. S., & Wagenmakers, E.-J. (in
press). Adjusting for publication bias in JASP &
R – selection models, PET-PEESE, and robust
Bayesian meta-analysis. *Advances in Methods and
Practices in Psychological Science*. https://doi.org/10.31234/osf.io/75bqn

Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., &
Wagenmakers, E.-J. (2021). A primer on Bayesian
model-averaged meta-analysis. *Advances in Methods and Practices in
Psychological Science*, *4*(3), 25152459211031256. https://doi.org/10.1177/25152459211031256

Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J.,
& Wagenmakers, E.-J. (2017). A Bayesian model-averaged
meta-analysis of the power pose effect with informed and default priors:
The case of felt power. *Comprehensive Results in Social
Psychology*, *2*(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760