See the documentation for your object's class:

estimate_slopes(
model,
trend = NULL,
levels = NULL,
transform = "response",
standardize = TRUE,
standardize_robust = FALSE,
ci = 0.95,
...
)
# S3 method for glmmTMB
estimate_slopes(
model,
trend = NULL,
levels = NULL,
transform = "response",
standardize = TRUE,
standardize_robust = FALSE,
ci = 0.95,
component = c("conditional", "zero_inflated", "zi"),
...
)

## Arguments

model |
A Bayesian model. |

trend |
A character vector indicating the name of the numeric variable
for which to compute the slopes. |

levels |
A character vector indicating the variables over which the
slope will be computed. If NULL (default), it will select all the remaining
predictors. |

transform |
Can be `"none"` (default for contrasts),
`"response"` (default for means), `"mu"` , `"unlink"` ,
`"log"` . `"none"` will leave the values on scale of the linear
predictors. `"response"` will transform them on scale of the response
variable. Thus for a logistic model, `"none"` will give estimations
expressed in log-odds (probabilities on logit scale) and `"response"`
in terms of probabilities. |

standardize |
If `TRUE` , adds standardized differences or
coefficients. |

standardize_robust |
Robust standardization through `MAD` (Median
Absolute Deviation, a robust estimate of SD) instead of regular `SD` . |

ci |
Credible Interval (CI) level. Default to 0.89 (89%). See
`ci` for further details. |

... |
Arguments passed to or from other methods. |

component |
A character vector indicating the model component for which
estimation is requested. Only applies to models from glmmTMB. Use
`"conditional"` for the count-model or `"zero_inflate"` or
`"zi"` for the zero-inflation model. |

## Value

A data frame of slopes.