Extract model coefficients, which are the sum of population-level
effects and corresponding group-level effects

```
# S3 method for brmsfit
coef(object, summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)
```

## Arguments

- object
An object of class `brmsfit`

.

- summary
Should summary statistics be returned
instead of the raw values? Default is `TRUE`

.

- robust
If `FALSE`

(the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If `TRUE`

, the median and the
median absolute deviation (MAD) are applied instead.
Only used if `summary`

is `TRUE`

.

- probs
The percentiles to be computed by the `quantile`

function. Only used if `summary`

is `TRUE`

.

- ...
Further arguments passed to `fixef.brmsfit`

and `ranef.brmsfit`

.

## Value

A list of 3D arrays (one per grouping factor).
If `summary`

is `TRUE`

,
the 1st dimension contains the factor levels,
the 2nd dimension contains the summary statistics
(see `posterior_summary`

), and
the 3rd dimension contains the group-level effects.
If `summary`

is `FALSE`

, the 1st dimension contains
the posterior draws, the 2nd dimension contains the factor levels,
and the 3rd dimension contains the group-level effects.

## Examples

```
if (FALSE) {
fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit),
data = epilepsy, family = gaussian(), chains = 2)
## extract population and group-level coefficients separately
fixef(fit)
ranef(fit)
## extract combined coefficients
coef(fit)
}
```