`summary.RoBTT`

creates summary tables for a
RoBTT object.

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

## Arguments

- object
a fitted 'RoBTT' object

- type
whether to show the overall 'RoBTT' 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 == "conditional"`

.

- group_estimates
show the model-averaged mean and standard deviation estimates for each group.

- 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.RoBTT`

returns a list of tables of class 'BayesTools_table'.

## Examples

```
if (FALSE) {
# using the example data from Darwin
data("fertilization", package = "RoBTT")
fit <- RoBTT(
x1 = fertilization$Self,
x2 = fertilization$Crossed,
prior_delta = prior("cauchy", list(0, 1/sqrt(2))),
prior_rho = prior("beta", list(3, 3)),
seed = 1,
chains = 1,
warmup = 1000,
iter = 2000,
control = set_control(adapt_delta = 0.95)
)
# 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")
}
```