`summary.RoBMA`

creates summary tables for a
RoBMA object.

```
# S3 method for RoBMA
summary(
object,
type = "ensemble",
conditional = FALSE,
output_scale = NULL,
probs = c(0.025, 0.975),
logBF = FALSE,
BF01 = FALSE,
short_name = FALSE,
remove_spike_0 = FALSE,
...
)
```

## Arguments

- object
a fitted RoBMA object

- type
whether to show the overall RoBMA results (`"ensemble"`

),
an overview of the individual models (`"models"`

), an overview of
the individual models MCMC diagnostics (`"diagnostics"`

), or a detailed summary
of the individual models (`"individual"`

). Can be abbreviated to first letters.

- conditional
show the conditional estimates (assuming that the
alternative is true). Defaults to `FALSE`

. Only available for
`type == "ensemble"`

.

- output_scale
transform the meta-analytic estimates to a different
scale. Defaults to `NULL`

which returns the same scale as the model was estimated on.

- probs
quantiles of the posterior samples to be displayed.
Defaults to `c(.025, .975)`

- logBF
show log of Bayes factors. Defaults to `FALSE`

.

- BF01
show Bayes factors in support of the null hypotheses. Defaults to
`FALSE`

.

- short_name
whether priors names should be shortened to the first
(couple) of letters. Defaults to `FALSE`

.

- remove_spike_0
whether spike prior distributions with location at zero should
be omitted from the summary. Defaults to `FALSE`

.

- ...
additional arguments

## Value

`summary.RoBMA`

returns a list of tables of class 'BayesTools_table'.

## Note

See `diagnostics()`

for visual convergence checks of the individual models.

## Examples

```
if (FALSE) {
# using the example data from Anderson et al. 2010 and fitting the default model
# (note that the model can take a while to fit)
fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels)
# summary can provide many details about the model
summary(fit)
# estimates from the conditional models can be obtained with
summary(fit, conditional = TRUE)
# overview of the models and their prior and posterior probability, marginal likelihood,
# and inclusion Bayes factor can be obtained with
summary(fit, type = "models")
# diagnostics overview, containing the maximum R-hat, minimum ESS, maximum MCMC error, and
# maximum MCMC error / sd across parameters for each individual model can be obtained with
summary(fit, type = "diagnostics")
# summary of individual models and their parameters can be further obtained by
summary(fit, type = "individual")
}
```