Amends an existing `bayesmanecfit`

object, for example, by
adding or removing fitted models.

```
amend(
object,
drop,
add,
loo_controls,
x_range = NA,
resolution = 1000,
sig_val = 0.01,
priors
)
```

- object
An object of class

`bayesmanecfit`

, as returned by`bnec`

.- drop
A

`character`

vector containing the names of model types you which to exclude for the modified fit.- add
A

`character`

vector containing the names of model types you which to include to the modified fit.- loo_controls
A named

`list`

of two elements ("fitting" and/or "weights"), each being a named`list`

containing the desired arguments to be passed on to`loo`

(via "fitting") or to`loo_model_weights`

(via "weights"). If "weights" is not provided by the user,`bnec`

will set the default`method`

argument in`loo_model_weights`

to "pseudobma". See ?`loo_model_weights`

for further info.- x_range
A range of predictor values over which to consider extracting ECx.

- resolution
The length of the predictor vector used for posterior predictions, and over which to extract ECx values. Large values will be slower but more precise.

- sig_val
Probability value to use as the lower quantile to test significance of the predicted posterior values against the lowest observed concentration (assumed to be the control), to estimate NEC as an interpolated NOEC value from smooth ECx curves.

- priors
An object of class

`brmsprior`

which specifies user-desired prior distributions of model parameters. If missing,`amend`

will figure out a baseline prior for each parameter. It can also be specified as a named`list`

where each name needs to correspond to the same string as`model`

. See Details.

All successfully fitted `bayesmanecfit`

model fits.

```
library(bayesnec)
data(manec_example)
exmp <- amend(manec_example, drop = "nec4param")
#> Only ecx4param is fitted, no model averaging done. Perhaps try setting better priors, or check ?show_params to make sure you have the correct parameter names for your priors.
#> Returning ecx4param
#> Warning: Found 5 observations with a pareto_k > 0.5 in model 'ecx4param'. We recommend to run more iterations to get at least about 2200 posterior draws to improve LOO-CV approximation accuracy.
#> Warning:
#> 5 (5.0%) p_waic estimates greater than 0.4. We recommend trying loo instead.
```